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

被引：0
|
作者：
马千里 [1 ]
郑启伦 [1 ]
彭宏 [1 ]
钟谭卫 [2 ]
覃姜维 [1 ]
机构：
[1] College of Computer Science and Engineering,South China University of Technology
[2] College of Science,South China Agriculture University
来源：
Chinese Physics B | 2008年 / 17卷 / 02期
关键词：
chaotic time series; multi-step-prediction; co-evolutionary strategy; recurrent neural networks;
D O I：
暂无
中图分类号：
O415.5 [混沌理论];
学科分类号：
070201 ;
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 co-evolutionary 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.
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页码：536 / 542
页数：7
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