Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Universal approximation using radial-basis-function networks
Neural Computation
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Information Sciences: an International Journal
Nonlinear adaptive prediction of complex-valued signals by complex-valued PRNN
IEEE Transactions on Signal Processing
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
IEEE Transactions on Wireless Communications
The Chebyshev-polynomials-based unified model neural networks forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear channel equalization for QAM signal constellation usingartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pipelined Recurrent Fuzzy Neural Networks for Nonlinear Adaptive Speech Prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Journal on Selected Areas in Communications
Toward an optimal PRNN-based nonlinear predictor
IEEE Transactions on Neural Networks
On the choice of parameters of the cost function in nested modular RNN's
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Application of the recurrent multilayer perceptron in modeling complex process dynamics
IEEE Transactions on Neural Networks
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A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper. In contrast to a feedforward artificial neural network (such as a functional link artificial neural network (FLANN)), the proposed PFLARNN consists of a number of simple small-scale functional link artificial recurrent neural network (FLARNN) modules. Since those modules of PFLARNN can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in its total computational efficiency. Moreover, nonlinearity of each module is introduced by enhancing the input pattern with nonlinear functional expansion. Therefore, the performance of the proposed filter can be further improved. Computer simulations demonstrate that with proper choice of functional expansion in the PFLARNN, this filter performs better than the FLANN and multilayer perceptron (MLP) for nonlinear dynamic system identification.