Nowadays, differential equations and their systems are one of the most preferred ways to represent models of dynamic objects. Objects from different areas are dynamic. Therefore, a great number of methods for dynamical systems identification have been developed. However, as processes become more complex, there exist a need to develop new tools. A change in the input action according to a predetermined law could be one of features for dynamic processes. The paper studies the efficiency of the method based on evolutionary algorithms to identify objects in the form of systems of differential equations with various input effects. Genetic programming and differential evolution are the algorithmic basis of the method. The method performs self-configuring of parameters for evolutionary algorithms. The presented paper studies efficiency of the proposed method on five problems where the initial objects are represented by systems of differential equations of various orders. The conducted study takes into account the presence of noise in the initial samples. It demonstrates graphical interpretation of the obtained results. The obtained results prove efficiency of the developed method under various input influences.
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27 February 2023
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Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
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Karaseva, T. S. (2023). Identification of Differential Equations Systems With Various Input Effects. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 152-159). European Publisher. https://doi.org/10.15405/epct.23021.19