A taxonomy for wavelet neural networks applied to nonlinear modelling

Authors:
Emilio Ribes-Gómez;Seán McLoone;George Irwin
Affiliations:
Department of Electrical Engineering, Universitat de València, Doctor Moliner 50, 46100 Burjassot, Spain;Department of Electronic Engineering, National University of Ireland, Maynooth, Co Kildare, Ireland;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT9 4AH, Northern Ireland, UK
Venue:
International Journal of Systems Science
Year:
2008

Citing 7
Cited 1

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A friendly guide to wavelets

A friendly guide to wavelets
Wavelets and subband coding

Wavelets and subband coding
On-line Successive Synthesis of Wavelet Networks

Neural Processing Letters
Computational Intelligence for Modelling, Control and Automation '99

Computational Intelligence for Modelling, Control and Automation '99
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Wavelet neural networks for function learning

IEEE Transactions on Signal Processing

Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.