Machine Learning Control by Symbolic Regression 2021
DOI: 10.1007/978-3-030-83213-1_4
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Symbolic Regression Methods

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Cited by 3 publications
(5 citation statements)
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References 12 publications
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“…If we replace a control vector u by the found control function v(t) in the right part of ODE system (1), then the obtained ODE system ẋ = f(x, v(t)) (7) will have a particular solution that reaches the given terminal state (4) from the given initial state (3) with the optimal value of the quality criterion (5). Let v * (t) be the optimal control function.…”
Section: The Extended Optimal Control Problemmentioning
confidence: 99%
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“…If we replace a control vector u by the found control function v(t) in the right part of ODE system (1), then the obtained ODE system ẋ = f(x, v(t)) (7) will have a particular solution that reaches the given terminal state (4) from the given initial state (3) with the optimal value of the quality criterion (5). Let v * (t) be the optimal control function.…”
Section: The Extended Optimal Control Problemmentioning
confidence: 99%
“…For more details on the network operator and the genetic algorithm that searches for the mathematical expression using the network operator, see the monographs [4,5].…”
Section: Symbolic Regressionmentioning
confidence: 99%
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“…The network operator method [21] was used in the calculations. This symbolic regression method is good because it uses the principle of variation of the basic solution, which significantly speeds up the process of finding a solution that is close to optimal.…”
Section: Stabilization System Synthesis For Rosbot In Gazebomentioning
confidence: 99%
“…For the selected object, the problem of synthesizing the stabilization system was successfully solved by machine learning based on symbolic regression via the network operator method [21].…”
Section: Introductionmentioning
confidence: 99%