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