Research on RBF Neural Network Method of Singularity Detection in Chaotic Time Series

摘要：

This paper researches on using RBF neural network for singularity detection in chaotic time series. History data are used for off-line training to RBF network and then comparing the result of output between actual and desire, if the difference of their values is above a certain threshold, the data will be judged singular. Parameters of RBF network will be renewed according to the real-time data. The raw data produced by Lorenz system, the data with disturbance and the measured data from oil pipeline pressure are used to test capacity of RBF network for singular signal anti-interference, weak signal examining and multi-step forecasting respectively. The research conclusion shows that RBF network not only has a strong ability for detecting faint signal in chaotic time series, but also has a good effect on singularity detection in measureddata.

关键词： RBF Singularity Detection Chaos Time series

作者: Jinhai Liu Jian Feng Fusheng Guan

作者单位: Department of Electrical and Computer Engineering University of Northeastem Shenyang, Liaoning Province, China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 893-898

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）