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论文类型：期刊论文

发表时间：2012-04-01

发表刊物：ACTA PHYSICA SINICA

收录刊物：SCIE、PKU、ISTIC、Scopus

卷号：61

期号：8

ISSN号：1000-3290

关键字：chaotic time series prediction; input variables selection; extreme learning machine; model selection

摘要：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.