Stochastic approximation methods for identification and control--A survey

Stochastic search techniques have been the essential part for most identification and self-organizing or learning control algorithms for stochastic systems. Stochastic approximation search algorithms have been very popular among the researchers in these areas because of their simplicity of implementation, convergence properties, as well as intuitive appeal to the investigator. This paper presents an exposition of the stochastic approximation algorithms and their application to various parameter identification and self-organizing control algorithms.