Genetic algorithm based variable-structure neural network and its industrial application

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

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

Abstract

This paper presents a neural network model with a variable structure, which is trained by an improved genetic algorithm (GA). The proposed variable-structure neural network (VSNN) consists of a Neural Network with Link Switches (NNLS) and a Network Switch Controller (NSC). In the NNLS, switches in its links between the hidden and output layers are introduced. By introducing the NSC to control the switches in the NNLS, the proposed neural network can model different input patterns with variable network structures. The proposed network gives better results and increased learning ability than conventional feed-forward neural networks. An industrial application on short-term load forecasting in Hong Kong is given to illustrate the merits of the proposed network.
Original languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
Pages1273-1278
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2004
EventIECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society - Busan, Korea, Republic of
Duration: 2 Nov 20046 Nov 2004

Conference

ConferenceIECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society
Country/TerritoryKorea, Republic of
CityBusan
Period2/11/046/11/04

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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