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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Control, Automation and Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.