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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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آمار تعداد مشاهده مقاله: 484 تعداد دریافت فایل اصل مقاله: 511 |
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