An improved SPSA algorithm for system identification using fuzzy rules for training neural networks

Authors:
Ahmad T. Abdulsadda;Kamran Iqbal
Affiliations:
Smart Micro-Systems Laboratory, Electrical and Computer Engineering, Michigan State University, East Lansing, USA 48823;Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, USA 72204
Venue:
International Journal of Automation and Computing
Year:
2011

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Abstract

Simultaneous perturbation stochastic approximation (SPSA) belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation (FASPA) algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks (NNs). Accordingly, a multilayer perceptron (MLP) network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.