Nonlinear prediction of chaotic signals using a normalised radial basis function network

Authors:
M. R. Cowper;B. Mulgrew;C. P. Unsworth
Affiliations:
Signals and Systems Research Group, Department of Electronics and Electrical Engineering, Edinburgh University, King's Buildings, Mayfield Road, EH9 3JL Edinburgh, Scotland, UK;Signals and Systems Research Group, Department of Electronics and Electrical Engineering, Edinburgh University, King's Buildings, Mayfield Road, EH9 3JL Edinburgh, Scotland, UK;Signals and Systems Research Group, Department of Electronics and Electrical Engineering, Edinburgh University, King's Buildings, Mayfield Road, EH9 3JL Edinburgh, Scotland, UK
Venue:
Signal Processing
Year:
2002

Citing 9
Cited 6

Numerical recipes in C (2nd ed.): the art of scientific computing

Numerical recipes in C (2nd ed.): the art of scientific computing
On radial basis function nets and kernel regression: statistical consistency, convergence rates, and receptive field size

Neural Networks
On functional approximation with normalized Gaussian units

Neural Computation
Radial basis function and related models: an overview

Signal Processing
Interference cancellation using radial basis function networks

Signal Processing
Adaptive filter theory (3rd ed.)

Adaptive filter theory (3rd ed.)
Neural networks for pattern recognition

Neural networks for pattern recognition
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate

IEEE Transactions on Neural Networks

Application of Bayesian trained RBF networks to nonlinear time-series modeling

Signal Processing - From signal processing theory to implementation
Steady-state performance constraints for dynamical models based on RBF networks

Engineering Applications of Artificial Intelligence
Novel Coupled Map Lattice Model for Prediction of EEG Signal

ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
FRBF neural network and new Smith predictor for wireless networked control systems

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A direct adaptive controller for EAF electrode regulator system using neural networks

Neurocomputing
Iterated time series prediction with multiple support vector regression models

Neurocomputing

Quantified Score

Hi-index	0.08

Visualization

Abstract

In this paper, a simple and robust combination of architecture and training strategy is proposed for a radial basis function network (RBFN). The proposed network uses a normalised Gaussian kernel architecture with kernel centres randomly selected from a training data set. The output layer weights are adapted using the numerically robust Householder transform. The application of this normalised radial basis function network (NRBFN) to the prediction of chaotic signals is reported. NRBFNs are shown to perform better than un-normalised equivalent networks for the task of chaotic signal prediction. Chaotic signal prediction is also used to demonstrate that a NRBFN is less sensitive to basis function parameter selection than an equivalent un-normalised network. A novel structure and training Strategy are proposed for a forward-backward RBFN (FB-RBFN). FB-NRBFN chaotic signal prediction results are compared with those for a NRBFN. Normalisation is found to be a simple alternative to regularisation for the task of using a RBFN to recursively predict, and thus to capture the dynamics of, a chaotic signal corrupted by additive white Gaussian noise.