Stochastic gradient-based hyperbolic orthogonal neural networks for nonlinear dynamic systems identification
Publish place: Journal of Mathematical Modeling، Vol: 10، Issue: 3
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMMO-10-3_011
تاریخ نمایه سازی: 19 خرداد 1403
Abstract:
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.
Keywords:
System identification , Piecewise continuous orthogonal functions , Hyperbolic orthogonal neural networks , Stochastic gradient descent
Authors
Ghasem Ahmadi
Department of Mathematics, Payame Noor University, P.O. Box ۱۹۳۹۵-۴۶۹۷, Tehran, Iran