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Machine learning control - Revision history
2025-07-02T23:51:51Z
Revision history for this page on the wiki
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Citation bot: Altered first2. Add: authors 1-1. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Dominic3203 | Linked from User:LinguisticMystic/cs/outline | #UCB_webform_linked 1238/2277
2025-04-17T01:19:11Z
<p>Altered first2. Add: authors 1-1. Removed parameters. Some additions/deletions were parameter name changes. | <a href="/wiki/Wikipedia:UCB" class="mw-redirect" title="Wikipedia:UCB">Use this bot</a>. <a href="/wiki/Wikipedia:DBUG" class="mw-redirect" title="Wikipedia:DBUG">Report bugs</a>. | Suggested by Dominic3203 | Linked from User:LinguisticMystic/cs/outline | #UCB_webform_linked 1238/2277</p>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref><ref name=":0">{{Cite book |<del style="font-weight: bold; text-decoration: none;">last</del>=Jiang |<del style="font-weight: bold; text-decoration: none;">first</del>=Yu |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119132677 |title=Robust Adaptive Dynamic Programming |last2=Jiang |first2=<del style="font-weight: bold; text-decoration: none;">Zhong‐Ping</del> |date=2017-05-30 |publisher=Wiley |isbn=978-1-119-13264-6 |edition=1 |language=en |doi=10.1002/9781119132677}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref><ref name=":0">{{Cite book |<ins style="font-weight: bold; text-decoration: none;">last1</ins>=Jiang |<ins style="font-weight: bold; text-decoration: none;">first1</ins>=Yu |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119132677 |title=Robust Adaptive Dynamic Programming |last2=Jiang |first2=<ins style="font-weight: bold; text-decoration: none;">Zhong-Ping</ins> |date=2017-05-30 |publisher=Wiley |isbn=978-1-119-13264-6 |edition=1 |language=en |doi=10.1002/9781119132677}}</ref></div></td>
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Citation bot
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1284548739&oldid=prev
SINIOINIS: Added a section on Adaptive Dynamic Programming
2025-04-08T09:29:05Z
<p>Added a section on Adaptive Dynamic Programming</p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 09:29, 8 April 2025</td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref><ref>{{Cite book |last=Jiang |first=Yu |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119132677 |title=Robust Adaptive Dynamic Programming |last2=Jiang |first2=Zhong‐Ping |date=2017-05-30 |publisher=Wiley |isbn=978-1-119-13264-6 |edition=1 |language=en |doi=10.1002/9781119132677}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref><ref<ins style="font-weight: bold; text-decoration: none;"> name=":0"</ins>>{{Cite book |last=Jiang |first=Yu |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119132677 |title=Robust Adaptive Dynamic Programming |last2=Jiang |first2=Zhong‐Ping |date=2017-05-30 |publisher=Wiley |isbn=978-1-119-13264-6 |edition=1 |language=en |doi=10.1002/9781119132677}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>== Adaptive Dynamic Programming ==</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Adaptive Dynamic Programming (ADP), also known as approximate dynamic programming or neuro-dynamic programming, is a machine learning control method that combines reinforcement learning with dynamic programming to solve optimal control problems for complex systems. ADP addresses the "[[curse of dimensionality]]" in traditional dynamic programming by approximating value functions or control policies using parametric structures such as neural networks. The core idea revolves around learning a control policy that minimizes a long-term cost function <math>J</math>, defined as <math>J(x(t)) = \int_{t}^{\infty} e^{-\gamma(\tau-t)} r(x(\tau), u(\tau)) \, d\tau</math> , where <math>x</math> is the system state, <math>u</math> is the control input, <math>r</math> is the instantaneous reward, and <math>\gamma</math> is a discount factor. ADP employs two interacting components: a critic that estimates the value function <math>V(x) \approx J(x)</math>, and an actor that updates the control policy <math>u(x)</math>. The critic and actor are trained iteratively using temporal difference learning or gradient descent to satisfy the [[Hamilton–Jacobi–Bellman equation|Hamilton-Jacobi-Bellman (HJB) equation]]: </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>where <math>f(x,u)</math> describes the system dynamics. Key variants include heuristic dynamic programming (HDP), dual heuristic programming (DHP), and globalized dual heuristic programming (GDHP).<ref name=":0" /> </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><br /></td>
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SINIOINIS
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1283273415&oldid=prev
SINIOINIS: fix typo
2025-03-31T13:45:45Z
<p>fix typo</p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 13:45, 31 March 2025</td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref><ref>{{Cite book |last=Jiang |first=Yu |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119132677 |title=Robust Adaptive Dynamic Programming |last2=Jiang |first2=Zhong‐Ping |date=2017-05-30 |publisher=Wiley |isbn=978-1-119-13264-6 |edition=1 |language=en |doi=10.1002/9781119132677<del style="font-weight: bold; text-decoration: none;">.</del>}}</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref><ref>{{Cite book |last=Jiang |first=Yu |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119132677 |title=Robust Adaptive Dynamic Programming |last2=Jiang |first2=Zhong‐Ping |date=2017-05-30 |publisher=Wiley |isbn=978-1-119-13264-6 |edition=1 |language=en |doi=10.1002/9781119132677}}</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Applications ==</div></td>
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SINIOINIS
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1283271858&oldid=prev
SINIOINIS: Add a reference for recent work
2025-03-31T13:31:04Z
<p>Add a reference for recent work</p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 13:31, 31 March 2025</td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as [[Regression analysis|regression]] problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[Neural network (machine learning)|Neural networks]] are commonly used for such tasks.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the [[Loss function|cost function]] of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893<ins style="font-weight: bold; text-decoration: none;"></ref><ref>{{Cite book |last=Jiang |first=Yu |url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119132677 |title=Robust Adaptive Dynamic Programming |last2=Jiang |first2=Zhong‐Ping |date=2017-05-30 |publisher=Wiley |isbn=978-1-119-13264-6 |edition=1 |language=en |doi=10.1002/9781119132677.}}</ins></ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Applications ==</div></td>
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SINIOINIS
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1254071392&oldid=prev
Perceptron599: General improvements
2024-10-29T08:34:56Z
<p>General improvements</p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 08:34, 29 October 2024</td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>{{short description|Subfield of machine learning, intelligent control<ins style="font-weight: bold; text-decoration: none;">,</ins> and control theory}}</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Machine learning control''' ('''MLC''') is a subfield of [[machine learning]], [[intelligent control]], and [[control theory]] which aims to solve [[optimal control]] problems with machine learning methods. Key applications are complex nonlinear systems for which [[linear control theory]] methods are not applicable.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Machine learning control''' ('''MLC''') is a subfield of [[machine learning]], [[intelligent control]], and [[control theory]] which aims to solve [[optimal control]] problems with machine learning methods. Key applications are complex nonlinear systems for which [[linear control theory]] methods are not applicable.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Types of problems and tasks ==</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Types of problems and tasks ==</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Four types of problems are commonly encountered<del style="font-weight: bold; text-decoration: none;">.</del></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Four types of problems are commonly encountered<ins style="font-weight: bold; text-decoration: none;">:</ins></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control parameter identification: MLC translates to a parameter identification<ref name=Baeck1993>Thomas Bäck & Hans-Paul Schwefel (Spring 1993) [http://doi.org/10.1162/evco.1993.1.1.1 "An overview of evolutionary algorithms for parameter optimization"], [[Evolutionary Computation (journal)|Journal of Evolutionary Computation (MIT Press)]], vol. 1, no. 1, pp. 1-23</ref> if the structure of the control law is given but the parameters are unknown. One example is the [[genetic algorithm]] for optimizing coefficients of a [[PID controller]]<ref name=Benard2015aiaa>N. Benard, J. Pons-Prats, J. Periaux, G. Bugeda, J.-P. Bonnet & E. Moreau, (2015) [https://arc.aiaa.org/doi/abs/10.2514/6.2015-2957 "Multi-Input Genetic Algorithm for Experimental Optimization of the Reattachment Downstream of a Backward-Facing Step with Surface Plasma Actuator"], Paper AIAA 2015-2957 at 46th AIAA Plasmadynamics and Lasers Conference, Dallas, TX, USA, pp. 1-23.</ref> or discrete-time optimal control.<ref>Zbigniew Michalewicz, Cezary Z. Janikow & Jacek B. Krawczyk (July 1992) [https://doi.org/10.1016/0898-1221(92)90094-X "A modified genetic algorithm for optimal control problems"], [Computers & Mathematics with Applications], vol. 23, no 12, pp. 83-94.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Control parameter identification: MLC translates to a parameter identification<ref name=Baeck1993>Thomas Bäck & Hans-Paul Schwefel (Spring 1993) [http://doi.org/10.1162/evco.1993.1.1.1 "An overview of evolutionary algorithms for parameter optimization"], [[Evolutionary Computation (journal)|Journal of Evolutionary Computation (MIT Press)]], vol. 1, no. 1, pp. 1-23</ref> if the structure of the control law is given but the parameters are unknown. One example is the [[genetic algorithm]] for optimizing coefficients of a [[PID controller]]<ref name=Benard2015aiaa>N. Benard, J. Pons-Prats, J. Periaux, G. Bugeda, J.-P. Bonnet & E. Moreau, (2015) [https://arc.aiaa.org/doi/abs/10.2514/6.2015-2957 "Multi-Input Genetic Algorithm for Experimental Optimization of the Reattachment Downstream of a Backward-Facing Step with Surface Plasma Actuator"], Paper AIAA 2015-2957 at 46th AIAA Plasmadynamics and Lasers Conference, Dallas, TX, USA, pp. 1-23.</ref> or discrete-time optimal control.<ref>Zbigniew Michalewicz, Cezary Z. Janikow & Jacek B. Krawczyk (July 1992) [https://doi.org/10.1016/0898-1221(92)90094-X "A modified genetic algorithm for optimal control problems"], [Computers & Mathematics with Applications], vol. 23, no 12, pp. 83-94.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the first kind:<del style="font-weight: bold; text-decoration: none;"> </del> MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]].<del style="font-weight: bold; text-decoration: none;"> A</del> [[<del style="font-weight: bold; text-decoration: none;">neural</del> network]] <del style="font-weight: bold; text-decoration: none;">is</del> commonly used<del style="font-weight: bold; text-decoration: none;"> technique</del> for <del style="font-weight: bold; text-decoration: none;">this</del> <del style="font-weight: bold; text-decoration: none;">task</del>.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
<td class="diff-marker" data-marker="+"></td>
<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Control design as <ins style="font-weight: bold; text-decoration: none;">[[Regression analysis|</ins>regression<ins style="font-weight: bold; text-decoration: none;">]]</ins> problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known [[full state feedback]]. [[<ins style="font-weight: bold; text-decoration: none;">Neural</ins> network<ins style="font-weight: bold; text-decoration: none;"> (machine learning)|Neural networks</ins>]] <ins style="font-weight: bold; text-decoration: none;">are</ins> commonly used for <ins style="font-weight: bold; text-decoration: none;">such</ins> <ins style="font-weight: bold; text-decoration: none;">tasks</ins>.<ref>C. Lee, J. Kim, D. Babcock & R. Goodman (1997) [https://dx.doi.org/10.1063/1.869290 "Application of neural networks to turbulence control for drag reduction"], [[Physics of Fluids]], vol. 6, no. 9, pp. 1740-1747</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the cost function of the plant. In this case, neither a model,<del style="font-weight: bold; text-decoration: none;"> nor</del> the control law structure,<del style="font-weight: bold; text-decoration: none;"> </del> nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the <ins style="font-weight: bold; text-decoration: none;">[[Loss function|</ins>cost function<ins style="font-weight: bold; text-decoration: none;">]]</ins> of the plant. In this case, neither a model, the control law structure, nor the optimizing actuation command needs to be known. The optimization is only based on the control performance (cost function) as measured in the plant. [[Genetic programming]] is a powerful regression technique for this purpose.<ref>D. C. Dracopoulos & S. Kent (December 1997) [http://doi.org/10.1007/BF01501508 "Genetic programming for prediction and control"], Neural Computing & Applications (Springer), vol. 6, no. 4, pp. 214-228.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref></div></td>
<td class="diff-marker"></td>
<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Reinforcement learning control: The control law may be continually updated over measured performance changes (rewards) using [[reinforcement learning]].<ref>Andrew G. Barto (December 1994) [http://doi.org/10.1016/0959-4388(94)90138-4 "Reinforcement learning control"], [[Current Opinion in Neurobiology]], vol. 6, no. 4, pp. 888–893</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>MLC comprises, for instance, neural network control, </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>genetic algorithm based control, </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>genetic programming control,</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>reinforcement learning control, </div></td>
<td colspan="2" class="diff-empty diff-side-added"></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>and has methodological overlaps with other data-driven control,</div></td>
<td colspan="2" class="diff-empty diff-side-added"></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>like [[artificial intelligence]] and [[robot control]].</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Applications ==</div></td>
<td class="diff-marker"></td>
<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Applications ==</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>MLC has been successfully applied</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>MLC has been successfully applied</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>to many nonlinear control problems,</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>to many nonlinear control problems,</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>exploring unknown and often unexpected actuation mechanisms.</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>exploring unknown and often unexpected actuation mechanisms.<ins style="font-weight: bold; text-decoration: none;"> Example applications include:</ins></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Example applications include</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
<td class="diff-marker"></td>
<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* <del style="font-weight: bold; text-decoration: none;">Attitude</del> control<del style="font-weight: bold; text-decoration: none;"> of satellites.</del><ref>Dimitris. C. Dracopoulos & [[Antonia J. Jones|Antonia. J. Jones]] (1994) </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* <ins style="font-weight: bold; text-decoration: none;">[[spacecraft attitude</ins> control<ins style="font-weight: bold; text-decoration: none;">]],</ins><ref>Dimitris. C. Dracopoulos & [[Antonia J. Jones|Antonia. J. Jones]] (1994) </div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[doi:10.1007/BF01414807|Neuro-genetic adaptive attitude control]], Neural Computing & Applications (Springer), vol. 2, no. 4, pp. 183-204.</ref></div></td>
<td class="diff-marker"></td>
<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[doi:10.1007/BF01414807|Neuro-genetic adaptive attitude control]], Neural Computing & Applications (Springer), vol. 2, no. 4, pp. 183-204.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>*<del style="font-weight: bold; text-decoration: none;"> Building</del> thermal control<del style="font-weight: bold; text-decoration: none;">.</del><ref>Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002) [http://doi.org/10.1016/S0378-7788(02)00071-3 "Optimization of building thermal design and control by multi-criterion genetic algorithm], [Energy and Buildings], vol. 34, no. 9, pp. 959-972.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* thermal control<ins style="font-weight: bold; text-decoration: none;"> of buildings,</ins><ref>Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002) [http://doi.org/10.1016/S0378-7788(02)00071-3 "Optimization of building thermal design and control by multi-criterion genetic algorithm], [Energy and Buildings], vol. 34, no. 9, pp. 959-972.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* <del style="font-weight: bold; text-decoration: none;">Feedback turbulence</del> control<del style="font-weight: bold; text-decoration: none;">.</del><ref name=Benard2015aiaa /><ref>Steven J. Brunton & Bernd R. Noack (2015) [http://doi.org/10.1115/1.4031175 Closed-loop turbulence control: Progress and challenges], [[Applied Mechanics Reviews]], vol. 67, no. 5, article 050801, pp. 1-48.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* <ins style="font-weight: bold; text-decoration: none;">feedback</ins> control<ins style="font-weight: bold; text-decoration: none;"> of [[turbulence]],</ins><ref name=Benard2015aiaa /><ref>Steven J. Brunton & Bernd R. Noack (2015) [http://doi.org/10.1115/1.4031175 Closed-loop turbulence control: Progress and challenges], [[Applied Mechanics Reviews]], vol. 67, no. 5, article 050801, pp. 1-48.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [[<del style="font-weight: bold; text-decoration: none;">Remotely</del> operated underwater vehicle]]s.<ref>J. Javadi-Moghaddam, & A. Bagheri (2010 [http://doi.org/10.1016/j.eswa.2009.06.015 "An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle"], [https://www.journals.elsevier.com/expert-systems-with-applications/ Expert Systems with Applications], vol. 37 no. 1, pp. 647-660.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>*<ins style="font-weight: bold; text-decoration: none;"> and</ins> [[<ins style="font-weight: bold; text-decoration: none;">remotely</ins> operated underwater vehicle]]s.<ref>J. Javadi-Moghaddam, & A. Bagheri (2010 [http://doi.org/10.1016/j.eswa.2009.06.015 "An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle"], [https://www.journals.elsevier.com/expert-systems-with-applications/ Expert Systems with Applications], vol. 37 no. 1, pp. 647-660.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">* </del>Many more engineering MLC application are summarized in the review article of PJ Fleming & RC Purshouse (2002).<ref>Peter J. Fleming, R. C. Purshouse (2002 [http://doi.org/10.1016/S0967-0661(02)00081-3 "Evolutionary algorithms in control systems engineering: a survey"]</div></td>
<td class="diff-marker" data-marker="+"></td>
<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Many more engineering MLC application are summarized in the review article of PJ Fleming & RC Purshouse (2002).<ref>Peter J. Fleming, R. C. Purshouse (2002 [http://doi.org/10.1016/S0967-0661(02)00081-3 "Evolutionary algorithms in control systems engineering: a survey"]</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[:nl:Control Engineering Practice|Control Engineering Practice]], vol. 10, no. 11, pp. 1223-1241</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[:nl:Control Engineering Practice|Control Engineering Practice]], vol. 10, no. 11, pp. 1223-1241</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>As for all general nonlinear methods,</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>As<ins style="font-weight: bold; text-decoration: none;"> is the case</ins> for all general nonlinear methods,</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>MLC <del style="font-weight: bold; text-decoration: none;">comes</del> <del style="font-weight: bold; text-decoration: none;">with</del> <del style="font-weight: bold; text-decoration: none;">no guaranteed</del> convergence, </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>MLC <ins style="font-weight: bold; text-decoration: none;">does</ins> <ins style="font-weight: bold; text-decoration: none;">not</ins> <ins style="font-weight: bold; text-decoration: none;">guarantee</ins> convergence, </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>optimality or robustness for a range of operating conditions.</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[Algorithmic efficiency|</ins>optimality<ins style="font-weight: bold; text-decoration: none;">]],</ins> or <ins style="font-weight: bold; text-decoration: none;">[[Robustness (computer science)|</ins>robustness<ins style="font-weight: bold; text-decoration: none;">]]</ins> for a range of operating conditions.</div></td>
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Perceptron599
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1254008040&oldid=prev
Perceptron599: Add See also
2024-10-29T00:24:14Z
<p>Add See also</p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 00:24, 29 October 2024</td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{short description|Subfield of machine learning, intelligent control and control theory}}</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''Machine learning control''' ('''MLC''') is a subfield of [[machine learning]], [[intelligent control]] and [[control theory]]</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''Machine learning control''' ('''MLC''') is a subfield of [[machine learning]], [[intelligent control]]<ins style="font-weight: bold; text-decoration: none;">,</ins> and [[control theory]]<ins style="font-weight: bold; text-decoration: none;"> which aims to solve [[optimal control]] problems with machine learning methods. Key applications are complex nonlinear systems for which [[linear control theory]] methods are not applicable.</ins></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>which solves [[optimal control]] problems with methods of [[machine learning]].</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>MLC comes with no guaranteed convergence, </div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>optimality or robustness for a range of operating conditions.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>optimality or robustness for a range of operating conditions.</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><br /></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>== See also ==</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [[Reinforcement learning]]</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== References ==</div></td>
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Perceptron599
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1178520214&oldid=prev
Rjjiii: remove default positional parameter (via WP:JWB)
2023-10-04T05:30:58Z
<p>remove default positional parameter (via <a href="/wiki/Wikipedia:JWB" class="mw-redirect" title="Wikipedia:JWB">WP:JWB</a>)</p>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>{{<del style="font-weight: bold; text-decoration: none;">Refbegin|1</del>}}</div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>{{<ins style="font-weight: bold; text-decoration: none;">refbegin</ins>}}</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Dimitris C Dracopoulos (August 1997) [https://www.springer.com/fr/book/9783540761617 "Evolutionary Learning Algorithms for Neural Adaptive Control"], Springer. {{ISBN|978-3-540-76161-7}}.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Dimitris C Dracopoulos (August 1997) [https://www.springer.com/fr/book/9783540761617 "Evolutionary Learning Algorithms for Neural Adaptive Control"], Springer. {{ISBN|978-3-540-76161-7}}.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Thomas Duriez, Steven L. Brunton & [[Bernd Noack|Bernd R. Noack]] (November 2016) [https://www.springer.com/fr/book/9783319406237 "Machine Learning Control - Taming Nonlinear Dynamics and Turbulence"], Springer. {{ISBN|978-3-319-40624-4}}.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Thomas Duriez, Steven L. Brunton & [[Bernd Noack|Bernd R. Noack]] (November 2016) [https://www.springer.com/fr/book/9783319406237 "Machine Learning Control - Taming Nonlinear Dynamics and Turbulence"], Springer. {{ISBN|978-3-319-40624-4}}.</div></td>
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Rjjiii
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1172788105&oldid=prev
WOSlinker: add {{refend}}
2023-08-29T09:31:32Z
<p>add {{refend}}</p>
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<td class="diff-marker"></td>
<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Dimitris C Dracopoulos (August 1997) [https://www.springer.com/fr/book/9783540761617 "Evolutionary Learning Algorithms for Neural Adaptive Control"], Springer. {{ISBN|978-3-540-76161-7}}.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Thomas Duriez, Steven L. Brunton & [[Bernd Noack|Bernd R. Noack]] (November 2016) [https://www.springer.com/fr/book/9783319406237 "Machine Learning Control - Taming Nonlinear Dynamics and Turbulence"], Springer. {{ISBN|978-3-319-40624-4}}.</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Thomas Duriez, Steven L. Brunton & [[Bernd Noack|Bernd R. Noack]] (November 2016) [https://www.springer.com/fr/book/9783319406237 "Machine Learning Control - Taming Nonlinear Dynamics and Turbulence"], Springer. {{ISBN|978-3-319-40624-4}}.</div></td>
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WOSlinker
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1144327025&oldid=prev
Jarble: linking
2023-03-13T03:21:01Z
<p>linking</p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 03:21, 13 March 2023</td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Building thermal control.<ref>Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002) [http://doi.org/10.1016/S0378-7788(02)00071-3 "Optimization of building thermal design and control by multi-criterion genetic algorithm], [Energy and Buildings], vol. 34, no. 9, pp. 959-972.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Building thermal control.<ref>Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002) [http://doi.org/10.1016/S0378-7788(02)00071-3 "Optimization of building thermal design and control by multi-criterion genetic algorithm], [Energy and Buildings], vol. 34, no. 9, pp. 959-972.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Feedback turbulence control.<ref name=Benard2015aiaa /><ref>Steven J. Brunton & Bernd R. Noack (2015) [http://doi.org/10.1115/1.4031175 Closed-loop turbulence control: Progress and challenges], [[Applied Mechanics Reviews]], vol. 67, no. 5, article 050801, pp. 1-48.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Feedback turbulence control.<ref name=Benard2015aiaa /><ref>Steven J. Brunton & Bernd R. Noack (2015) [http://doi.org/10.1115/1.4031175 Closed-loop turbulence control: Progress and challenges], [[Applied Mechanics Reviews]], vol. 67, no. 5, article 050801, pp. 1-48.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* Remotely operated <del style="font-weight: bold; text-decoration: none;">under water</del> vehicle.<ref>J. Javadi-Moghaddam, & A. Bagheri (2010 [http://doi.org/10.1016/j.eswa.2009.06.015 "An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle"], [https://www.journals.elsevier.com/expert-systems-with-applications/ Expert Systems with Applications], vol. 37 no. 1, pp. 647-660.</ref></div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* <ins style="font-weight: bold; text-decoration: none;">[[</ins>Remotely operated <ins style="font-weight: bold; text-decoration: none;">underwater</ins> vehicle<ins style="font-weight: bold; text-decoration: none;">]]s</ins>.<ref>J. Javadi-Moghaddam, & A. Bagheri (2010 [http://doi.org/10.1016/j.eswa.2009.06.015 "An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle"], [https://www.journals.elsevier.com/expert-systems-with-applications/ Expert Systems with Applications], vol. 37 no. 1, pp. 647-660.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Many more engineering MLC application are summarized in the review article of PJ Fleming & RC Purshouse (2002).<ref>Peter J. Fleming, R. C. Purshouse (2002 [http://doi.org/10.1016/S0967-0661(02)00081-3 "Evolutionary algorithms in control systems engineering: a survey"]</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Many more engineering MLC application are summarized in the review article of PJ Fleming & RC Purshouse (2002).<ref>Peter J. Fleming, R. C. Purshouse (2002 [http://doi.org/10.1016/S0967-0661(02)00081-3 "Evolutionary algorithms in control systems engineering: a survey"]</div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[:nl:Control Engineering Practice|Control Engineering Practice]], vol. 10, no. 11, pp. 1223-1241</ref></div></td>
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Jarble
https://en.wikipedia.org/w/index.php?title=Machine_learning_control&diff=1130896618&oldid=prev
89.200.40.146: /* Applications */
2023-01-01T14:26:56Z
<p><span class="autocomment">Applications</span></p>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* <del style="font-weight: bold; text-decoration: none;">Atitude</del> control of satellites.<ref>Dimitris. C. Dracopoulos & [[Antonia J. Jones|Antonia. J. Jones]] (1994) </div></td>
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<td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* <ins style="font-weight: bold; text-decoration: none;">Attitude</ins> control of satellites.<ref>Dimitris. C. Dracopoulos & [[Antonia J. Jones|Antonia. J. Jones]] (1994) </div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[doi:10.1007/BF01414807|Neuro-genetic adaptive attitude control]], Neural Computing & Applications (Springer), vol. 2, no. 4, pp. 183-204.</ref></div></td>
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<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Building thermal control.<ref>Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002) [http://doi.org/10.1016/S0378-7788(02)00071-3 "Optimization of building thermal design and control by multi-criterion genetic algorithm], [Energy and Buildings], vol. 34, no. 9, pp. 959-972.</ref></div></td>
<td class="diff-marker"></td>
<td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Building thermal control.<ref>Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002) [http://doi.org/10.1016/S0378-7788(02)00071-3 "Optimization of building thermal design and control by multi-criterion genetic algorithm], [Energy and Buildings], vol. 34, no. 9, pp. 959-972.</ref></div></td>
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