Learning algorithms for a neural network with laterally inhibited receptive fields

Qiang Gan*, Jun Yao, K. R. Subramanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a neural network with its output layer as a classifier and its hidden layer constrained by laterally inhibited receptive fields as feature extractor, in which the idea that wavelet transforms are very suitable for modeling the primary visual information processing is reflected. Two learning algorithms for designing the receptive fields are proposed. The problem associated with local minima caused by the inherent oscillatory property in laterally inhibited receptive fields is combated in the algorithm using discrete wavelets. Good performance is obtained in the experiment of ECG signal classification using the neural network.

Original languageEnglish (US)
Title of host publicationIEEE World Congress on Computational Intelligence
Editors Anon
PublisherIEEE
Pages1156-1161
Number of pages6
Volume2
StatePublished - Jan 1 1998
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: May 4 1998May 9 1998

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period5/4/985/9/98

ASJC Scopus subject areas

  • Software

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