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Maximum likelihood forgetting stochastic gradient estimation algorithm for Hammerstein CARARMA systems

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4 Author(s)
Junhong Li ; School of Electrical Engineering, Nantong University, 226019, China ; Juping Gu ; Weiguo Ma ; Rui Ding

This paper considers the identification problem of Hammerstein CARARMA systems, and derives a maximum likelihood stochastic gradient algorithm (ML-SG) by using the maximum likelihood principle and the negative gradient search. Furthermore, a forgetting factor is introduced to improve the convergence rate of the ML-SG algorithm. The simulation results indicate that the proposed algorithm are effective.

Published in:

2012 24th Chinese Control and Decision Conference (CCDC)

Date of Conference:

23-25 May 2012