Function estimation using a neural-fuzzy network and an improved genetic algorithm

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

doi:10.1016/j.ijar.2003.10.008

Function estimation using a neural-fuzzy network and an improved genetic algorithm

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

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

4 Citations (Scopus)

Abstract

This paper presents the estimation of the transmission gains for an AC power line data network in an intelligent home. The estimated gains ensure the transmission reliability and efficiency. A neural-fuzzy network with rule switches is proposed to perform the estimation. An improved genetic algorithm is proposed to tune the parameters and the rules of the proposed neural-fuzzy network. By turning on or off the rule switches, an optimal rule base can be obtained. An application example will be given.

Original language	English
Pages (from-to)	243-260
Number of pages	18
Journal	International Journal of Approximate Reasoning
Volume	36
Issue number	3
DOIs	https://doi.org/10.1016/j.ijar.2003.10.008
Publication status	Published - 1 Jul 2004

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

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10.1016/j.ijar.2003.10.008

Cite this

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AB - This paper presents the estimation of the transmission gains for an AC power line data network in an intelligent home. The estimated gains ensure the transmission reliability and efficiency. A neural-fuzzy network with rule switches is proposed to perform the estimation. An improved genetic algorithm is proposed to tune the parameters and the rules of the proposed neural-fuzzy network. By turning on or off the rule switches, an optimal rule base can be obtained. An application example will be given.

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Function estimation using a neural-fuzzy network and an improved genetic algorithm

Abstract

ASJC Scopus subject areas

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