A variable-parameter neural network trained by improved genetic algorithm and its application

S. H. Ling, H. K. Lam, Hung Fat Frank Leung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper presents a neural network with variable parameters. These variable parameters adapt to the changes of the input environment, and tackle different input data sets in a large domain. Each input data set is effectively handled by its corresponding set of network parameters. Thus, the proposed neural network exhibits a better learning and generalization ability than a traditional one. An improved genetic algorithm [1] is proposed to train the network parameters. An application example on hand-written pattern recognition will be presented to verify and illustrate the improvement.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages1343-1348
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 31 Jul 20054 Aug 2005

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period31/07/054/08/05

ASJC Scopus subject areas

  • Software

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