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

emission

HAWK

construct

jaunty

lern bei der selbstständigen 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 geprägten Perspektive orientieren.978-3-658-05098-6978-3-658-05099-3

废除

Insensate

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

propose

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

熄灭

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

Conserve

逃避现实

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