Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network

作     者：马千里 郑启伦 彭宏 钟谭卫 覃姜维 

作者机构：College of Computer Science and EngineeringSouth China University of Technology College of ScienceSouth China Agriculture University 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2008年第17卷第2期

页      面：536-542页

核心收录：

学科分类：07[理学] 070201[理学-理论物理] 0702[理学-物理学] 

基　　金：Project supported by the State Key Program of National Natural Science of China (Grant No 30230350) the Natural Science Foundation of Guangdong Province,China (Grant No 07006474) 

主　　题：chaotic time series multi-step-prediction co-evolutionary strategy recurrent neural networks 

摘      要：This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets： the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.