Alleviating ‘overfitting’ via genetically-regularised neural network

Chan, Z.S.H., Ngan, H.W., Rad, A.B., & Ho, T.K. (2002) Alleviating ‘overfitting’ via genetically-regularised neural network. Electronics Letters, 38(15), pp. 809-810.

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A hybrid genetic algorithm/scaled conjugate gradient regularisation method is designed to alleviate ANN `over-fitting'. In application to day-ahead load forecasting, the proposed algorithm performs better than early-stopping and Bayesian regularisation, showing promising initial results.

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2 citations in Scopus
1 citations in Web of Science®
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ID Code: 38213
Item Type: Journal Article
Refereed: Yes
Keywords: Neural network, System forecast
DOI: 10.1049/el:20020592
ISSN: 0013-5194
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Engineering and Theory (080607)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Power and Energy Systems Engineering (excl. Renewable Power) (090607)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2002 The Institution of Engineering and Technology
Deposited On: 27 Oct 2010 23:55
Last Modified: 12 Jul 2017 01:02

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