Stochastic gradient-based hyperbolic orthogonal neural networks for nonlinear dynamic systems identification

	Stochastic gradient-based hyperbolic orthogonal neural networks for nonlinear dynamic systems identification
Journal of Mathematical Modeling
دوره 10، شماره 3، آذر 2022، صفحه 529-547 اصل مقاله (271.45 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22124/jmm.2022.21572.1890
نویسنده
Ghasem Ahmadi^*
Department of Mathematics, Payame Noor University, P.O. Box 19395-4697, Tehran, Iran
چکیده
Orthogonal neural networks (ONNs) are some powerful types of the neural networks in the modeling of non-linearity. They are constructed by the usage of orthogonal functions sets. Piecewise continuous orthogonal functions (PCOFs) are some important classes of orthogonal functions. In this work, based on a set of hyperbolic PCOFs, we propose the hyperbolic ONNs to identify the nonlinear dynamic systems. We train the proposed neural models with the stochastic gradient descent learning algorithm. Then, we prove the stability of this algorithm. Simulation results show the efficiencies of proposed model.
کلیدواژه‌ها
System identification؛ piecewise continuous orthogonal functions؛ hyperbolic orthogonal neural networks؛ stochastic gradient descent

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