Automatic differential equations identification by self-configuring genetic programming algorithm

The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information n...

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Veröffentlicht in:IOP conference series. Materials Science and Engineering Jg. 734; H. 1; S. 12093 - 12100
1. Verfasser: Karaseva, T S
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.01.2020
Schlagworte:
Differential equations
Genetic algorithms
Mathematical analysis
ISSN:1757-8981, 1757-899X
Online-Zugang:Volltext
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Zusammenfassung:The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information needed is only the dynamic system initial point and the sample of input and output effects. The stability of the proposed approach to the presence of noise in the sample and the small amount of data is investigated.
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ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/734/1/012093