Maximum likelihood forgetting stochastic gradient estimation algorithm for Hammerstein CARARMA systems

摘要：

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.

关键词： Parameter estimation Hammerstein models Maximum likelihood Stochastic gradient

作者: Junhong Li Juping Gu Weiguo Ma Rui Ding

作者单位: School of Electrical Engineering,Nantong University, Nantong 226019, PR China. School of Electrical Engineering, Nantong University, Nantong 226019, PR China.

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2545-2550

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）