A chaotic time series prediction method based on fuzzy neural network and its application
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia（G-P） algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of input nodes of FNN, minimum embedding dimension obtained from chaotic time series analysis is used to design FNN. It was proved from two study cases that the proposed model is efficient in the practical prediction of chaotic time series.
Zhuo Chen Chen Lu Wenjin Zhang Xiaowei Du
School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, P.R.China
国际会议
2010国际混沌、分形理论与应用研讨会(IWCFTA 2010)
昆明
英文
355-359
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）