Auxiliary model based recursive extended least squares and maximum likelihood estimation algorithms for input nonlinear systems

摘要：

This paper studies the identification problems of input nonlinear controlled autoregressive moving average (INCARMA) systems, and derived an auxiliary model based recursive extended least squares (AM-RELS) algorithm and a maximum likelihood algorithm based on the Newton optimization method. The simulation results show that the proposed algorithm are effective.

关键词： Recursive identification Least squares Hammerstein model Maximum likelihood estimation Newton method

作者: LI Junhong JIANG Ping ZHU Hairong DING Rui

作者单位: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan Univ School of Electrical Engineering, Nantong University, Nantong 226019, China School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 1848-1853

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）