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Numerical Solution of Machine Learning Control Problems

In: Machine Learning Control by Symbolic Regression

Author

Listed:
  • Askhat Diveev

    (Russian Academy of Sciences (FRC CSC RAS), Federal Research Center “Computer Science and Control”)

  • Elizaveta Shmalko

    (Russian Academy of Sciences (FRC CSC RAS), Federal Research Center “Computer Science and Control”)

Abstract

This chapter discusses general issues in the numerical solution of machine learning control problems. As parametric machine learning approach, the most 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 functions based on the methods of symbolic regression is presented. To overcome computational difficulties, it is proposed to use the principle of small variations. A description of the genetic algorithm is given as the main search mechanism in the space of structures, and in addition, it can also be used to adjust the parameters of a given structure of a function in parametric search.

Suggested Citation

  • Askhat Diveev & Elizaveta Shmalko, 2021. "Numerical Solution of Machine Learning Control Problems," Springer Books, in: Machine Learning Control by Symbolic Regression, chapter 0, pages 27-53, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-83213-1_3
    DOI: 10.1007/978-3-030-83213-1_3
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