FIR Neural Networks Adaptive Prediction of Chaotic Time Series

摘要：

In this paper, based on the FIR nonlinear adaptive filters and the deterministic and nonlinear characterization of chaotic time series, the FIR spacetime neural networks nonlinear adaptive predictive fil ters are proposed to predict chaotic time series. The prediction model of chaotic time series is established with the FIR neural networks and the steps of the learning algorithm with the FIR neural networks are expressed. The prediction model and the learning algo- rithm are more effective and reliable than the adaptive higher-order nonlinear FIR filter. The Experimental and simulating results show that the FIR neural networks enjoy good adaptive prediction performance and can be successfully used to predict chaotic time series.

作者: Li-sheng Yin Xi-yue Huang Chang-cheng Xiang

作者单位: College of Automation University of Chongqing Chongqing,China, 400030 College of Automation University of Chongqing Chongqing,China, 400030;Department of mathematics Hube

会议类型: 国际会议

会议名称: 第三届国际脉冲动力系统及应用学术会议

会议地点: 青岛

会议语种:英文

在线出版日期: 2006-07-21（万方平台首次上网日期，不代表论文的发表时间）