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

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dc.contributor.author Ling, SS
dc.contributor.author Leung, FH
dc.contributor.author Lam, H
dc.date.accessioned 2012-02-02T04:28:13Z
dc.date.issued 2007-01
dc.identifier.citation Soft Computing, 2007, 11 (11), pp. 1033 - 1052
dc.identifier.issn 1432-7643
dc.identifier.other C1UNSUBMIT 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.
dc.publisher Springer
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1007/s00500-007-0151-5
dc.rights The original publication is available at www.springerlink.com en_US
dc.subject Neural network · Real-coded genetic algorithm · Short-term load forecasting, Hand-written recognition, Artificial Intelligence & Image Processing
dc.subject Neural network · Real-coded genetic algorithm · Short-term load forecasting, Hand-written recognition; Artificial Intelligence & Image Processing
dc.title Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications
dc.type Journal Article
dc.parent Soft Computing
dc.journal.volume 11
dc.journal.volume 11
dc.journal.number 11 en_US
dc.publocation New York en_US
dc.identifier.startpage 1033 en_US
dc.identifier.endpage 1052 en_US
dc.cauo.name FEIT. A/DRsch Ctre for Health Technologies en_US
dc.conference Verified OK en_US
dc.for 0102 Applied Mathematics
dc.personcode 106694 en_US
dc.personcode 0000059012 en_US
dc.personcode 0000059682 en_US
dc.percentage 100 en_US
dc.classification.name Applied Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity WOS:000247391600002 en_US
dc.location.activity WOS:000288851300047
dc.location.activity WOS:000247391600002
dc.location.activity WOS:000247391600002
dc.location.activity WOS:000247391600002
dc.description.keywords Neural network ? Real-coded genetic algorithm ? Short-term load forecasting, Hand-written recognition en_US
dc.description.keywords Conversion
dc.description.keywords Neural network · Real-coded genetic algorithm · Short-term load forecasting, Hand-written recognition
dc.description.keywords Neural network · Real-coded genetic algorithm · Short-term load forecasting, Hand-written recognition
dc.description.keywords Neural network · Real-coded genetic algorithm · Short-term load forecasting, Hand-written recognition
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies


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