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Combinational problem decomposition method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction

Nand, Ravneil and Naseem, Mohammed and Reddy, Emmenual and Sharma, Bibhya N. (2017) Combinational problem decomposition method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction. [Conference Proceedings]

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    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 combination utilization of two hybrid problem decomposition method for Elman recurrent neural networks and applied to time series prediction. The results reveal that the proposed method has got better results in some datasets when compared to its standalone methods. The results are better in selected cases for proposed method when compared to several other approaches from the literature.

    Item Type: Conference Proceedings
    Additional Information: Accepted for Publishing
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Fulori Nainoca
    Date Deposited: 21 Mar 2018 12:23
    Last Modified: 11 Jun 2018 09:49
    URI: http://repository.usp.ac.fj/id/eprint/10623
    UNSPECIFIED

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