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    Advanced adaptive nonmonotone conjugate gradient training algorithm for recurrent neural networks

    Peng, C.C. and Magoulas, George D. (2008) Advanced adaptive nonmonotone conjugate gradient training algorithm for recurrent neural networks. International Journal of Artificial Intelligence Tools 17 (5), pp. 963-984. ISSN 0218-2130.

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    Abstract

    Recurrent networks constitute an elegant way of increasing the capacity of feedforward networks to deal with complex data in the form of sequences of vectors. They are well known for their power to model temporal dependencies and process sequences for classification, recognition, and transduction. In this paper we propose an advanced nonmonotone Conjugate Gradient training algorithm for recurrent neural networks, which is equipped with an adaptive tuning strategy for both the nonmonotone learning horizon and the stepsize. Simulation results in sequence processing using three different recurrent architectures demonstrate that this modification of the Conjugate Gradient method is more effective than previous attempts.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Conjugate gradient methods, global convergence, nonmonotone linesearch, adaptive learning, training algorithms, recurrent neural networks, sequence processing
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 04 Feb 2011 15:51
    Last Modified: 02 Dec 2016 13:23
    URI: http://eprints.bbk.ac.uk/id/eprint/1873

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