Parameter Estimation using Simultaneous Perturbation Stochastic Approximation

Abstract

The simultaneous perturbation stochastic approximation (SPSA) is an extension of Kiefer-Wolfowitz stochastic approximation algorithm. In SPSA, since all parameters are perturbed simultaneously, it is possible to modify parameters with only two measurements of an evaluation function regardless of the dimension of the parameter. We propose a parameter estimation algorithm using the SPSA. Convergence theorem for the proposed algorithm is shown. A simulation result also reveals a feasibility of the identification scheme proposed here.