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论文类型：期刊论文

发表时间：2017-10-01

发表刊物：NEURAL PROCESSING LETTERS

收录刊物：Scopus、SCIE、EI

卷号：46

期号：2

页面范围：705-717

ISSN号：1370-4621

关键字：Variable selection; Extreme learning machine; Partial least square regression; Multivariate chaotic time series

摘要：In this paper, a novel method (Hybrid-ELM-PLSR) is proposed based on hybrid variable selection algorithm and improved extreme learning machine (ELM) for multivariate chaotic time series prediction. The hybrid variable selection algorithm combines the advantages of filter and wrapper, effectively balancing the calculation speed and prediction accuracy. Moreover, for ELM, multicollinearity, which can result in ill-condition, is always existent among the hidden layer output matrix. And the optimal number of hidden nodes is also difficult to be determined. Therefore,in order to overcome these problems, an improved ELM (ELM-PLSR) is proposed based on partial least square regression (PLSR). It can effectively enhance the stability performance and prediction performance of ELM. Hybrid-ELM-PLSR can be divided into three stages. At first, filter is used to rearrange the input variables through the correlations with desired variables. Then wrapper is used to select the optimal variable subset through evaluating the prediction performance of different subsets. Finally, ELM-PLSR is used to build the prediction model. The simulation experiment results based on San Francisco river runoff dataset demonstrate that the proposed method is effective for multivariate chaotic time series. And the prediction accuracy and reliability are higher than other methods.