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

Lijuan Liu, Feng Ding*, Cheng Wang, Ahmed Alsaedi, and Tasawar Hayat
International Journal of Control, Automation, and Systems, vol. 16, no. 5, pp.2528-2537, 2018

Abstract : "This paper focuses on the parameter estimation problems of multivariate equation-error systems. A multiinnovation 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."

Keyword : Maximum likelihood, multi-innovation, multivariate system, stochastic gradient.

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