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Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction

Chandra, Rohitash (2013) Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction. [Conference Proceedings]

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    Abstract

    Cooperative coevolution employs different problem decomposition methods to decompose the neural network problem into subcomponents. The efficiency of a problem decomposition method is dependent on the neural network architecture and the nature of the training problem. The adaptation of problem decomposition methods have been recently proposed which showed that different problem decomposition methods are needed at different phases in the evolutionary process. This paper employs an adaptive cooperative coevolution problem decomposition framework for training recurrent neural networks on chaotic time series problems. The Mackey Glass, Lorenz and Sunspot chaotic time series are used. The results show improvement in performance when compared to cooperative coevolution and other methods from literature.

    Item Type: Conference Proceedings
    Additional Information: DOI: 10.1109/IJCNN.2013.6706997
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Rohitash Chandra
    Date Deposited: 24 Jan 2014 11:33
    Last Modified: 14 Jun 2016 15:10
    URI: http://repository.usp.ac.fj/id/eprint/5686
    UNSPECIFIED

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