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A method to resolve the overfitting problem in recurrent neural networks for prediction of complex system's behavior

Mahdaviani, K, Mazyar, H, Majidi, S and Saraee, M 2008, A method to resolve the overfitting problem in recurrent neural networks for prediction of complex system's behavior , in: IEEE World Congress on Computational Intelligence / IEEE International Joint Conference on Neural Networks, 1-6 June 2008, Hong Kong, China.

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    Abstract

    In this paper a new method to resolve the overfitting problem for predicting complex systems' behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural networks and using simulated annealing for the optimization of their generalization. The major work is done based on the idea of ensemble neural networks. Finally the results of using this method on two sample datasets are presented and the effectiveness of this method is illustrated.

    Item Type: Conference or Workshop Item (Paper)
    Additional Information: Print ISBN: 978-1-4244-1820-6
    Themes: Media, Digital Technology and the Creative Economy
    Memory, Text and Place
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Journal or Publication Title: Proceedings of IEEE World Congress on Computational Intelligence/IEEE International Joint Conference on Neural Networks
    Publisher: IEEE
    Refereed: Yes
    Depositing User: Dr Mo Saraee
    Date Deposited: 26 Oct 2011 14:48
    Last Modified: 20 Aug 2013 18:16
    URI: http://usir.salford.ac.uk/id/eprint/18693

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