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)


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
Number of pages6
Publication statusPublished - 1 Dec 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 31 Jul 20054 Aug 2005


ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
CityMontreal, QC

ASJC Scopus subject areas

  • Software

Cite this