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Advanced RNN based NARMA predictors

Mandic, DP and Chambers, JA (2000) Advanced RNN based NARMA predictors Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 26 (1). pp. 105-117.

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An analysis of nonlinear time series prediction schemes, realised though advanced Recurrent Neural Network (RNN) techniques is provided. Due to practical constraints in using common RNNs, such as the problem of vanishing gradient, some other ways to improve RNN based prediction are analysed. This is undertaken for a simple RNN through to the Pipelined Recurrent Neural Network (PRNN), which consists of a number of nested small-scale RNNs. A Nonlinear AutoRegressive Moving Average (NARMA) nonlinear model is introduced in the context of RNN architectures, and an posteriori mode of operation within that framework. Moreover, it is shown that the basic a priori PRNN structure exhibits certain a posteriori features. The PRNN based predictor, is shown to exhibit nesting, and to be able to represent block cascaded stochastic models, such as the Wiener-Hammerstein model. Simulations undertaken on a speech signal support the analysis.

Item Type: Article
Divisions : Surrey research (other units)
Authors :
Mandic, DP
Date : 1 August 2000
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:27
Last Modified : 24 Jan 2020 23:57

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