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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Vydané v:IOP conference series. Materials Science and Engineering Ročník 734; číslo 1; s. 12093 - 12100
Hlavný autor: Karaseva, T S
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.01.2020
Predmet:
Differential equations
Genetic algorithms
Mathematical analysis
ISSN:1757-8981, 1757-899X
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/734/1/012093