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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 38–48Cite as

Neuron-Network Level Problem Decomposition Method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9949))

Abstract

The breaking down of a particular problem through problem decomposition has enabled complex problems to be solved efficiently. The two major problem decomposition methods used in cooperative coevolution are synapse and neuron level. The combination of both the problem decomposition as a hybrid problem decomposition has been seen applied in time series prediction. The different problem decomposition methods applied at particular area of a network can share its strengths to solve the problem better, which forms the major motivation. In this paper, we are proposing a problem decomposition method that combines neuron and network level problem decompositions for Elman recurrent neural networks and applied to time series prediction. The results reveal that the proposed method has got better results in few datasets when compared to two popular standalone methods. The results are better in selected cases for proposed method when compared to several other approaches from the literature.

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Authors and Affiliations

  1. School of Computing Information and Mathematical Sciences, University of South Pacific, Suva, Fiji

    Ravneil Nand, Emmenual Reddy & Mohammed Naseem

Authors
  1. Ravneil Nand
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  2. Emmenual Reddy
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  3. Mohammed Naseem
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Corresponding author

Correspondence to Ravneil Nand .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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© 2016 Springer International Publishing AG

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Nand, R., Reddy, E., Naseem, M. (2016). Neuron-Network Level Problem Decomposition Method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-46675-0_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46674-3

  • Online ISBN: 978-3-319-46675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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