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Modified Neuron-Synapse level problem decomposition method for Cooperative Coevolution of Feedforward Networks for Time Series Prediction

Nand, Ravneil and Sharma, Bibhya N. (2017) Modified Neuron-Synapse level problem decomposition method for Cooperative Coevolution of Feedforward Networks for Time Series Prediction. [Conference Proceedings]

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    Abstract

    Complex problems have been solved efficiently through decomposition of a particular problem using problem decompositions. Even combination of different distinct problem decomposition methods has shown good results in time series prediction. The application of different problem decomposition methods at different stages of a network can share its strengths to solve the problem in hand better. Hybrid versions of two distinct problem decomposition methods has showed promising results in past. In this paper, a modified version of latterly introduced Neuron-Synapse level problem decomposition is proposed using feedforward neural networks for time series prediction. The results shows that the proposed modified model has got better results in more datasets when compared to its previous version. The results are better in some cases for proposed method in comparison to several other methods 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 12:02
    Last Modified: 11 Jun 2018 09:54
    URI: http://repository.usp.ac.fj/id/eprint/10622
    UNSPECIFIED

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