Theory and design of adaptive filters
Theory and design of adaptive filters
Predicting time series by a fully connected neural network trained by back propagation
Computing & Control Engineering Journal
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computer Architecture and Parallel Processing
Computer Architecture and Parallel Processing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Nonlinear prediction of speech
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Nonlinear adaptive prediction of speech with a pipelined recurrentneural network
IEEE Transactions on Signal Processing
Nonlinear adaptive prediction of nonstationary signals
IEEE Transactions on Signal Processing
Computational capabilities of recurrent NARX neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Journal on Selected Areas in Communications
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
Toward an optimal PRNN-based nonlinear predictor
IEEE Transactions on Neural Networks
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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.