Parameter estimation using simultaneous perturbation stochastic approximation

The simultaneous perturbation stochastic approximation (SPSA) is an extension of the 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...

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Bibliographic Details
Published in:Electrical engineering in Japan 2006-01, Vol.154 (2), p.30-39
Main Authors: Hirokami, Tatsuya, Maeda, Yutaka, Tsukada, Hiroyuki
Format: Article
Language:English
Subjects:
identification
optimization
parameter estimation
simultaneous perturbation
stochastic approximation
Online Access:Get full text
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Summary:The simultaneous perturbation stochastic approximation (SPSA) is an extension of the 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. A convergence theorem for the proposed algorithm is shown. A simulation result also reveals the feasibility of the identification scheme proposed here. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 154(2): 30–39, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20239
ISSN:0424-7760
1520-6416
DOI:10.1002/eej.20239