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JOURNALS // Intelligent systems. Theory and applications // Archive

Intelligent systems. Theory and applications, 2022 Volume 26, Issue 1, Pages 35–43 (Mi ista331)

Part 1. Plenary reports

Machine learning of intelligent control systems

A. I. Diveev

Federal Research Center ”Computer Science and Control” of the Russian Academy of Sciences

Abstract: Machine learning intelligent control systems by symbolic regression methods is considered. Symbolic regression allows to find mathematical expressions for various problems where it is necessary to find structure and parameters of unknown multidimensional function. The search for an unknown function is carried out by a genetic algorithm in code space of symbolic regression method. Functions containing condition operators that are mandatory component of intelligent control systems programs.

Keywords: symbolic regression, control synthesis, machine learning, optimal control.



© Steklov Math. Inst. of RAS, 2024