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An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution

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Abstract

In this paper, an integrated model based on efficient extreme learning machine (EELM) and differential evolution (DE) is proposed to predict chaotic time series. In the proposed model, a novel learning algorithm called EELM is presented and used to model the chaotic time series. The EELM inherits the basic idea of extreme learning machine (ELM) in training single hidden layer feedforward networks, but replaces the commonly used singular value decomposition with a reduced complete orthogonal decomposition to calculate the output weights, which can achieve a much faster learning speed than ELM. Moreover, in order to obtain a more accurate and more stable prediction performance for chaotic time series prediction, this model abandons the traditional two-stage modeling approach and adopts an integrated parameter selection strategy which employs a modified DE algorithm to optimize the phase space reconstruction parameters of chaotic time series and the model parameter of EELM simultaneously based on a hybrid validation criterion. Experimental results show that the proposed integrated prediction model can not only provide stable prediction performances with high efficiency but also achieve much more accurate prediction results than its counterparts for chaotic time series prediction.

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Acknowledgments

This work is supported by the Key Program of the National Natural Science Foundation of China (Grant No. 61139002), the National High Technology Research and Development Program of China (Grant No. 2012AA063301), the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No.2014BAJ04B02), the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant Nos. 3122014D032, 3122013P013), the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (Grant No. CAAC-ITRB-201401). All of these supports are appreciated.

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Wei Guo & Tao Xu

  2. Information Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, 300300, China

    Tao Xu & Zonglei Lu

  3. School of Computer Science and Technology, Civil Aviation University of China, Tianjin, 300300, China

    Tao Xu & Zonglei Lu

  4. School of Information Science and Technology, Yancheng Teachers University, Yancheng, 224002, China

    Wei Guo

Authors
  1. Wei Guo
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  2. Tao Xu
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Correspondence to Wei Guo.

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Guo, W., Xu, T. & Lu, Z. An integrated chaotic time series prediction model based on efficient extreme learning machine and differential evolution. Neural Comput & Applic 27, 883–898 (2016). https://doi.org/10.1007/s00521-015-1903-2

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