| Issue |
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|
|
|---|---|---|
| Article Number | 04012 | |
| Number of page(s) | 5 | |
| Section | Adaptive Intelligence: Exploring Learning in Evolutionary Algorithms and Neural Networks | |
| DOI | https://doi.org/10.1051/itmconf/20245904012 | |
| Published online | 25 January 2024 | |
Application of the evolutionary approach to structural and parametric identification of dynamic objects
Siberian Federal University, Department of Business Informatics and Business Process Modeling,
Krasnoyarsk,
660074,
Russia
* Corresponding author: tatyanakarasewa@yandex.ru
The paper examines the evolutionary approach to structural and parametric identification of dynamic systems in the form of differential equations. The approach is based on a genetic programming algorithm to determine a structure of the equation and differential evolution method for parameters selection. The author proposed approach based on such evolutionary algorithms as genetic programming and differential evolution. The search for the structure is carried out by genetic programming. The selection of numerical parameters and initial conditions is implemented by a method of differential evolution. The problem of finding a model that describes changes in the efficiency of a hydraulic system is solved with the help of this approach. The proposed approach is compared with a recurrent neural network and a nonparametric kernel regression estimation.
© The Authors, published by EDP Sciences, 2024
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