Intelligent neural network design for nonlinear control using simultaneous perturbation stochastic approximation (SPSA) optimization
Articolo
Data di Pubblicazione:
2016
Citazione:
Intelligent neural network design for nonlinear control using simultaneous perturbation stochastic approximation (SPSA) optimization / A. Dineva, A.R. Várkonyi Kóczy, J.K. Tar, V. Piuri. - In: JAPANESE JOURNAL OF APPLIED PHYSICS. - ISSN 1347-4065. - (2016), pp. 011612-1-011612-6. ((Intervento presentato al 14. convegno Global Research and Education, Inter-Academia tenutosi a Hamamatsu nel 2016 [10.7567/JJAPCP.4.011612].
Abstract:
Recently intelligent control systems using neural networks (NN) have been widely applied. NNs are used to approximate complicated mathematical functions of nonlinear systems. This paper
considers the design of an intelligent NN controller for nonlinear systems where the neural network is trained with the simultaneous perturbation stochastic approximation (SPSA) algorithm
instead of the classical training methods. The main contribution of the SPSA method that it requires only two objective function measurements per iteration regardless of the dimension of
the optimization problem. The effectiveness of the proposed scheme is demonstrated by the adaptive control of the translational oscillator / rotational actuator (TORA) system. Results of
numerical simulation substantiate that the suggested approach leads to a fast way of controller designs by providing acceptable performance.
Tipologia IRIS:
01 - Articolo su periodico
Elenco autori:
A. Dineva, A.R. Várkonyi Kóczy, J.K. Tar, V. Piuri
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