Adaptive filter theory
Theory and applications of adaptive second order IIR Volterra filters
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Automatica (Journal of IFAC)
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
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Fast adaptive digital equalization by recurrent neural networks
IEEE Transactions on Signal Processing
IEEE Transactions on Wireless Communications
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
An improved recurrent neural network for M-PAM symbol detection
IEEE Transactions on Neural Networks
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
Decision feedback recurrent neural equalization with fast convergence rate
IEEE Transactions on Neural Networks
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
Using recurrent neural networks for adaptive communication channel equalization
IEEE Transactions on Neural Networks
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To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.