Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm

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

doi:10.1109/IECON.2002.1185266

Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm

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

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

This paper presents the estimation of the transmission gain for the AC power line data network in an intelligent home. The estimated gain ensures the transmission reliability and efficiency. A neural network with link switches is proposed to perform the estimation. Genetic algorithm with arithmetic- crossover and non-uniform mutation is employed to tune the parameters and the structure of the proposed neural network. An application example will be given.

Original language	English
Title of host publication	IECON Proceedings (Industrial Electronics Conference)
Pages	1926-1929
Number of pages	4
DOIs	https://doi.org/10.1109/IECON.2002.1185266
Publication status	Published - 1 Dec 2002
Event	Proceedings of the 2002 28th Annual Conference of the IEEE Industrial Electronics Society - Sevilla, Spain Duration: 5 Nov 2002 → 8 Nov 2002

Conference

Conference	Proceedings of the 2002 28th Annual Conference of the IEEE Industrial Electronics Society
Country	Spain
City	Sevilla
Period	5/11/02 → 8/11/02

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

More information

10.1109/IECON.2002.1185266

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title = "Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm",

abstract = "This paper presents the estimation of the transmission gain for the AC power line data network in an intelligent home. The estimated gain ensures the transmission reliability and efficiency. A neural network with link switches is proposed to perform the estimation. Genetic algorithm with arithmetic- crossover and non-uniform mutation is employed to tune the parameters and the structure of the proposed neural network. An application example will be given.",

author = "Lam, {H. K.} and Ling, {S. H.} and Leung, {Hung Fat Frank} and Tam, {P. K.S.} and Lee, {Y. S.}",

year = "2002",

month = dec,

day = "1",

doi = "10.1109/IECON.2002.1185266",

language = "English",

pages = "1926--1929",

booktitle = "IECON Proceedings (Industrial Electronics Conference)",

note = "Proceedings of the 2002 28th Annual Conference of the IEEE Industrial Electronics Society ; Conference date: 05-11-2002 Through 08-11-2002",

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Lam, HK, Ling, SH, Leung, HFF, Tam, PKS & Lee, YS 2002, Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm. in IECON Proceedings (Industrial Electronics Conference). pp. 1926-1929, Proceedings of the 2002 28th Annual Conference of the IEEE Industrial Electronics Society, Sevilla, Spain, 5/11/02. https://doi.org/10.1109/IECON.2002.1185266

Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm. / Lam, H. K.; Ling, S. H.; Leung, Hung Fat Frank; Tam, P. K.S.; Lee, Y. S.

IECON Proceedings (Industrial Electronics Conference). 2002. p. 1926-1929.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This paper presents the estimation of the transmission gain for the AC power line data network in an intelligent home. The estimated gain ensures the transmission reliability and efficiency. A neural network with link switches is proposed to perform the estimation. Genetic algorithm with arithmetic- crossover and non-uniform mutation is employed to tune the parameters and the structure of the proposed neural network. An application example will be given.

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Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm

Abstract

Conference

ASJC Scopus subject areas

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