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From an a priori RNN to an a posteriori PRNN nonlinear predictor

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conference contribution
posted on 18.01.2010 by Danilo P. Mandic, Jonathon Chambers
We provide an analysis of nonlinear time series prediction schemes, from a common recurrent neural network (RNN) to the pipelined recurrent neural network (PRNN), which consists of a number of nested small-scale RNNs. All these schemes are shown to be suitable for nonlinear autoregressive moving average (NARMA) prediction. The time management policy of such prediction schemes is addressed and classified in terms of a priori and a posteriori mode of operation. Moreover, it is shown that the basic a priori PRNN structure exhibits certain a posteriori features. In search for an optimal PRNN based predictor, some inherent features of the PRNN, such as nesting and the choice of cost function are addressed. It is shown that nesting in essence is an a posteriori technique which does not diverge. Simulations undertaken on a speech signal support the algorithms derived, and outperform linear least mean square and recursive least squared predictors

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

MANDIC, D.P. and CHAMBERS, J., 1998. From an a priori RNN to an a posteriori PRNN nonlinear predictor. IN: Proceedings of the 1998 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing VIII, Cambridge, 31st August-2nd September 1988, pp. 174-183

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© IEEE

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VoR (Version of Record)

Publication date

1998

Notes

This is a conference paper [© IEEE]. It 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.

ISBN

078035060X

ISSN

1089-3555

Language

en

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