Adaptive initial step size selection for Simultaneous Perturbation Stochastic Approximation

Keiichi Ito, Tom Dhaene

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

A difficulty in using Simultaneous Perturbation Stochastics Approximation (SPSA) is its performance sensitivity to the step sizes chosen at the initial stage of the iteration. If the step size is too large, the solution estimate may fail to converge. The proposed adaptive stepping method automatically reduces the initial step size of the SPSA so that reduction of the objective function value occurs more reliably. Ten mathematical functions each with three different noise levels were used to empirically show the effectiveness of the proposed idea. A parameter estimation example of a nonlinear dynamical system is also included.
Original languageEnglish
Article number200
Pages (from-to)1-18
JournalSpringerPlus
Volume5
Issue number1
DOIs
Publication statusPublished - 1 Dec 2016
Externally publishedYes

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