Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

Ling, SH; Leung, FHF; Lam, HK

Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

Ling, SH

Leung, FHF Lam, HK

Permalink

Publication Type:: Journal Article
Citation:: Soft Computing, 2007, 11 (11), pp. 1033 - 1052
Issue Date:: 2007-09-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (497.38 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Lam, HK	en_US
dc.date.issued	2007-09-01	en_US
dc.identifier.citation	Soft Computing, 2007, 11 (11), pp. 1033 - 1052	en_US
dc.identifier.issn	1432-7643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14530
dc.description.abstract	This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7-31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement. © Springer-Verlag 2007.	en_US
dc.relation.ispartof	Soft Computing	en_US
dc.relation.isbasedon	10.1007/s00500-007-0151-5	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	WOS:000247391600002	en_US
dc.location.activity	WOS:000288851300047
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7-31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement. © Springer-Verlag 2007.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/14530