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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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering Vol. 734; no. 1; pp. 12093 - 12100
Main Author: Karaseva, T S
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.01.2020
Subjects:
Differential equations
Genetic algorithms
Mathematical analysis
ISSN:1757-8981, 1757-899X
Online Access:Get full text
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Summary: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