Extended stochastic approximation algorithms for systems parameters identification

The paper presents an approach to derive stochastic approximation type algorithms used within system identification schemes. The technique proposed enables one to derive recursive identification algorithms under fairly mild assumptions with respect to noises and disturbances corrupting the system´s. The algorithms obtained do not involve inversion of the identification criterion Hessian, and are stable with respect to variation of the Hessian rank. Examples presented demonstrate preferable convergence properties of the algorithms obtained with respect to conventional recursive schemes.