Identification of Nonlinear Time Varying Systems Using Recurrent Neural Networks

摘要：

In this paper the identification using recurrent neural networks based on extended Kalman filter is presented. As it is well known, it is difficult to identify a nonlinear time varying system using traditional identification approaches. Although there have been some network architectures and learning algorithms for the nonlinear time variant systems, the lagged orders must be estimated There is no need for a priori knowledge for the lagged orders in the recurrent networks. In this paper the learning algorithm has the fast convergence of the extended Kalman filter and needs no estimate of the lag in the system in the presented recurrent networks. Simulation results demonstrate the effectiveness and the fast convergence and good tracking capability of this approach.

关键词： recurrent neural networks extended kalman filter system identification nonlinear time varying system.

作者: Gao-feng Zou Zheng-ou Wang

作者单位: Institute of System Engineering, Tianjin University Tianjin 300072 China

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 649-653

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）