Control Synthesis as Machine Learning Control by Symbolic Regression Methods

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@Article{shmalko:2021:AS,

• author = "Elizaveta Shmalko and Askhat Diveev",
• title = "Control Synthesis as Machine Learning Control by Symbolic Regression Methods",
• journal = "Applied Sciences",
• year = "2021",
• volume = "11",
• number = "12",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2076-3417",
• URL = "https://www.mdpi.com/2076-3417/11/12/5468",
• DOI = "doi:10.3390/app11125468",
• abstract = "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.",
• notes = "also known as \cite{app11125468}",

}

Genetic Programming entries for Elizaveta Yu Shmalko Askhat Diveev Ibraghimovich

Citations