Advanced RNN Based NARMA Predictors
Journal of VLSI Signal Processing Systems
Nonlinear and Noisy Time Series Prediction Using a Hybrid Nonlinear Neural Predictor
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
New Radial Basis Function Neural Network Training for Nonlinear and Nonstationary Signals
Computational Intelligence and Security
A novel adaptive nonlinear filter-based pipelined feed-forward second-order Volterra architecture
IEEE Transactions on Signal Processing
A novel adaptive bilinear filter based on pipelined architecture
Digital Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Information Sciences: an International Journal
Hi-index | 35.69 |
New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined recurrent neural network (PRNN) are presented. A computationally efficient gradient descent (GD) learning algorithm, together with a novel extended recursive least squares (ERLS) learning algorithm, are proposed. Simulation studies based on three speech signals that have been made public and are available on the World Wide Web (WWW) are used to test the nonlinear predictor. The gradient descent algorithm is shown to yield poor performance in terms of prediction error gain, whereas consistently improved results are achieved with the ERLS algorithm. The merit of the nonlinear predictor structure is confirmed by yielding approximately 2 dB higher prediction gain than a linear structure predictor that employs the conventional recursive least squares (RLS) algorithm