Abstract
We consider a problem of mathematical modeling and computer simulation of nonlinear controlled dynamical systems represented by differential-algebraic equations of index 2. The solution of the problem is proposed within the framework of a neural network based semi-empirical approach that combines theoretical knowledge of the modeling object with training tools applied to artificial neural networks. We propose particular form semi-empirical models implementing implicit Runge-Kutta integration formulas inside the activation function. The training of the semi-empirical model makes it possible to elaborate on the models of aerodynamic coefficients implemented as a part of it. We present a semi-empirical model that uses as theoretical knowledge the equations of a full model of hypersonic vehicle motion in the specific phase of descent in the atmosphere. The simulation results for the problem of identifying the aerodynamic coefficient, implemented as an ANN-module of a semi-empirical model of the movement of a hypersonic vehicle, are presented.
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Acknowledgments
This research is supported by the Ministry of Science and Higher Education of the Russian Federation as Project No. 9.7170.2017/8.9.
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Kozlov, D.S., Tiumentsev, Y.V. (2020). Semi-empirical Neural Network Models of Hypersonic Vehicle 3D-Motion Represented by Index 2 DAE. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_39
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DOI: https://doi.org/10.1007/978-3-030-30425-6_39
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