Abstract
The problems of neural network algorithms designing for modeling of the switching technical systems are considered. The method for designing of dynamic models using polynomial approximation is proposed. The generalized models of switching systems taking into account nonstationary perturbations are constructed. The heuristic optimization algorithms implemented in the form of computer libraries are developed. The problems of neural network algorithm implementation using high-level hybrid computing are considered. The effect of the application of the algorithms proposed in the paper is to reduce the time and energy costs on creating vector thrust. Algorithmic support based on artificial neural networks with training is proposed. The library of high-parallel training algorithms for neural networks in the problems of optimal trajectories constructing for switching technical systems is developed. The obtained results can be used in problems of researching models of controlled technical systems with switching operating modes, in particular, in modeling the dynamics of various classes of aircraft and in modeling intelligent transport systems #CSOC1120.
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Druzhinina, O.V., Masina, O.N., Petrov, A.A., Lisovsky, E.V., Lyudagovskaya, M.A. (2020). Neural Network Optimization Algorithms for Controlled Switching Systems. In: Silhavy, R. (eds) Artificial Intelligence and Bioinspired Computational Methods. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1225. Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_39
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DOI: https://doi.org/10.1007/978-3-030-51971-1_39
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