Titlebook: Machine Learning Control by Symbolic Regression; Askhat Diveev,Elizaveta Shmalko Book 2021 The Editor(s) (if applicable) and The Author(s)

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lern bei der selbstst?ndigen Regulation von Lernprozessen konfrontiert. Zudem wird die Einstellung zur Selbstregulation erfasst. Die Ergebnisse weisen darauf hin, dass sich Lehrende mit positiverer Einstellung eher an einer humanistisch gepr?gten Perspektive orientieren.978-3-658-05098-6978-3-658-05099-3

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Introduction,ocess of control, about artificial intelligence and machine learning, and, of course, about symbolic regression methods, which open up new possibilities not only in the field of control automation, but also in the design of completely different optimal structures, including building structures, tech

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Mathematical Statements of MLC Problems,finding an unknown functional relationship. Next, we present the formulations of control theory problems that can be distinguished as machine learning control problems, namely the optimal control problem and more widely the general control synthesis problem, optimal control problem based on the synt

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Numerical Solution of Machine Learning Control Problems,t popular and widespread apparatus of neural networks is considered. Theoretical substantiations are given for the general possibility of using machine learning methods for searching functions, namely the Kolmogorov–Arnold theorem. The only general approach of structural-parametric search of functio

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Examples of MLC Problem Solutions, book. First, the tasks of unsupervised learning are considered based on the value of the target functional. The classical Pontryagin problem is considered and a comparison of the solution obtained by machine learning with the classical result is given. The problem of stabilization system synthesis