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On the choice of parameters of the cost function in nested modular RNN's

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posted on 15.01.2010, 11:04 authored by Danilo P. Mandic, Jonathon Chambers
We address the choice of the coefficients in the cost function of a modular nested recurrent neural-network (RNN) architecture, known as the pipelined recurrent neural network (PRNN). Such a network can cope with the problem of vanishing gradient, experienced in prediction with RNN’s. Constraints on the coefficients of the cost function, in the form of a vector norm, are considered. Unlike the previous cost function for the PRNN, which included a forgetting factor motivated by the recursive least squares (RLS) strategy, the proposed forms of cost function provide “forgetting” of the outputs of adjacent modules based upon the network architecture. Such an approach takes into account the number of modules in the PRNN, through the unit norm constraint on the coefficients of the cost function of the PRNN. This is shown to be particularly suitable, since due to inherent nesting in the PRNN, every module gives its full contribution to the learning process, whereas the unit norm constrained cost function introduces a sense of forgetting in the memory management of the PRNN. The PRNN based upon a modified cost function outperforms existing PRNN schemes in the time series prediction simulations presented.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

MANDIC, D.P. and CHAMBERS, J.A., 2000. On the choice of parameters of the cost function in nested modular RNN's. IEEE Transactions on Neural Networks, 11(2), pp. 315 - 322

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2000

Notes

This article was published in the journal IEEE Transactions on Neural Networks [© IEEE] and is also available at: http://ieeexplore.ieee.org/. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISSN

1045-9227

Language

en