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Indexed by:期刊论文

Date of Publication:2012-04-01

Journal:ACTA PHYSICA SINICA

Included Journals:SCIE、PKU、ISTIC、Scopus

Volume:61

Issue:8

ISSN No.:1000-3290

Key Words:chaotic time series prediction; input variables selection; extreme learning machine; model selection

Abstract:For multivariate chaotic time series prediction problem, a prediction based on input variable selection and extreme learning machine is proposed in this paper. The multivariate chaotic time series is reconstructed in phase space, and a mutual information based method is used to select the input variables, which have high statistics information with the output variables. The extreme learning machine is conducted to model the multivariate chaotic time series in the phase space by utilizing its approximation capability. In order to improve the prediction accuracy, a model selection algorithm is conducted for extreme learning machine to choose an expected minimum risk prediction model. Simulation results based on Lorenz, Rossler multivariate chaotic time series and Rossler hyperchaotic time series show the effectiveness of the proposed method.