物理学报2012，Vol.61Issue(8)：97-105,9.
基于极端学习机的多变量混沌时间序列预测
Multivariate chaotic time series prediction based on extreme learning machine
摘要
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, Rrssler multivariate chaotic time series and Rrssler hyperchaotic time series show the effectiveness of the proposed method.关键词
混沌时间序列预测/输入变量选择/极端学习机/模型选择Key words
chaotic time series prediction/input variables selection/extreme learning machine/model selectionPACS: 05.45.Tp分类
信息技术与安全科学引用本文复制引用
王新迎,韩敏..基于极端学习机的多变量混沌时间序列预测[J].物理学报,2012,61(8):97-105,9.基金项目
国家自然科学基金（批准号：61074096）资助的课题. （）
