Complete Binary Variational Analytic Programming for Synthesis of Control at Dynamic Constraints

Diveev, Askhat; Shmalko, Elizaveta

doi:10.1051/itmconf/20171002004

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The binary complete genetic programming (BCGP), unlike the genetic programming, has codes of the same length for different mathematical expressions [10]. The graph of BCGP for the example (30) is presented in Figure 3.…”

Section: Binary Complete Genetic Programmingmentioning

confidence: 99%

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Control Synthesis as Machine Learning Control by Symbolic Regression Methods

Shmalko

Diveev

2021

Applied Sciences

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The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.

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Section: Binary Complete Genetic Programmingmentioning

confidence: 99%

“…The genetic programming (GP) [9] was the first symbolic regression method capable to search for mathematical expressions. Now there is a variety of such methods [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%