Open AccessHybrid approach to the dynamic systems identification based on the self-configuring genetic programming algorithm and the differential evolution method
Author(s) -
Tatiana Karaseva,
О. Э. Семенкина
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012076
Subject(s) - differential evolution , identification (biology) , computer science , differential (mechanical device) , genetic programming , dynamic programming , genetic algorithm , differential equation , algorithm , meta optimization , differential dynamic programming , dynamical systems theory , value (mathematics) , mathematical optimization , mathematics , artificial intelligence , optimization problem , machine learning , engineering , physics , mathematical analysis , botany , quantum mechanics , biology , aerospace engineering
This paper considers a hybrid approach to the identification of dynamical systems based on a self-configuring genetic programming algorithm and a differential evolution method. The value of this approach is in the automatic determination of the order, structure and parameters of differential equation, i.e., a model of a dynamic system. The application of the differential evolution method can significantly increase the accuracy of the resulting model confirmed by the results of the experiments presented in this paper.