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

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

doi:10.1109/IECON.2004.1431759

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

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

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-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 language	English
Title of host publication	IECON Proceedings (Industrial Electronics Conference)
Pages	1273-1278
Number of pages	6
DOIs	https://doi.org/10.1109/IECON.2004.1431759
Publication status	Published - 1 Dec 2004
Event	IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society - Busan, Korea, Republic of Duration: 2 Nov 2004 → 6 Nov 2004

Conference

Conference	IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society
Country/Territory	Korea, Republic of
City	Busan
Period	2/11/04 → 6/11/04

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/IECON.2004.1431759

http://hdl.handle.net/10397/1374

Cite this

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title = "Genetic algorithm based variable-structure neural network and its industrial application",

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.",

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

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AB - 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.

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DO - 10.1109/IECON.2004.1431759

M3 - Conference article published in proceeding or book

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BT - IECON Proceedings (Industrial Electronics Conference)

T2 - IECON 2004 - 30th Annual Conference of IEEE Industrial Electronics Society

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ER -

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

Abstract

Conference

ASJC Scopus subject areas

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