Maximum Likelihood Multi-innovation Stochastic Gradient Estimation for Multivariate Equation-error Systems

Abstract

This paper focuses on the parameter estimation problems of multivariate equation-error systems. A multi-innovation generalized extended stochastic gradient algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification subsystems, each of which has only a parameter vector, and a coupled subsystem maximum likelihood multi-innovation stochastic gradient identification algorithm is developed for estimating the parameter vectors of these subsystems. The simulation results show that the coupled subsystem maximum likelihood multi-innovation stochastic gradient algorithm can generate more accurate parameter estimates and has faster convergence rates compared with the multi-innovation generalized extended stochastic gradient algorithm.