Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network
Ma Qian-Lia, Zheng Qi-Luna, Peng Honga, Qin Jiang-Weia, Zhong Tan-Weib
a College of Computer Science and Engineering,
South China
University of Technology, Guangzhou 510640, China; b College of Science, South China Agriculture University,
Guangzhou 510640, China
Abstract 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.
Received: 04 June 2007
Revised: 07 July 2007
Published: 20 February 2008
Fund: Project supported by the State Key
Program of National Natural Science of China (Grant No 30230350) and
the Natural Science Foundation
of Guangdong Province, China (Grant No 07006474).
Cite this article:
Ma Qian-Li, Zheng Qi-Lun, Peng Hong, Zhong Tan-Wei, Qin Jiang-Wei Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network 2008 Chin. Phys. B 17 536
