Tuning of the structure and parameters of neural network using an improved genetic algorithm

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

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

45 Citations (Scopus)

Abstract

This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). The improved GA is implemented by floating-point number. The processing time of the improved GA is faster than that of the GA implemented by binary number as coding and decoding are not necessary. By introducing new genetic operators to the improved GA, it will also be shown that the improved GA performs better than the traditional GA based on some benchmark test functions. A neural network with switches introduced to links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure. Using the improved GA, the structure and the parameters of the neural network can be tuned. An application example on sunspot forecasting is given to show the merits of the improved GA and the proposed neural network.
Original languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
Pages25-30
Number of pages6
Publication statusPublished - 1 Dec 2001
Event27th Annual Conference of the IEEE Industrial Electronics Society IECON'2001 - Denver, CO, United States
Duration: 29 Nov 20012 Dec 2001

Conference

Conference27th Annual Conference of the IEEE Industrial Electronics Society IECON'2001
CountryUnited States
CityDenver, CO
Period29/11/012/12/01

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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