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

  • Ravneil NandEmail author
  • Emmenual Reddy
  • Mohammed Naseem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Cooperative coevolution Problem decomposition Recurrent network Time series prediction 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Computing Information and Mathematical SciencesUniversity of South PacificSuvaFiji

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