Stochastic Approximation Algorithms for System Identification Using Normal Operating Data

Abstract

This paper considers the identification problem of linear control systems by use of stochastic approximation. There are two approaches to constructing models based on stochastic approximation: dynamical model approach and finite memory model one. In the former approach Saridis and Stein [1] have obtained the most general results. And in the latter approach Holmes [2] has established an algorithm giving an unbiased estimate. But it is common with these two works that (i) only white noise sequence is allowed for the input sequence and (ii) the updating of the estimates occurs at every finite time interval.