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Improving training of radial basis function network for classification of power quality disturbances

Citation

Hoang, TA and Nguyen, DT, Improving training of radial basis function network for classification of power quality disturbances, Electronics Letters, 38, (17) pp. 976-977. ISSN 0013-5194 (2002) [Refereed Article]

DOI: doi:10.1049/el:20020658

Abstract

Features extracted from non-stationary and transitory power quality disturbances using wavelet transform modulus maxima can serve as powerful discriminating features for wavelet-based classification of these disturbances. Using these features, a comprehensive 'knowledge-based' algorithm is proposed for the training of the radial basis function network classifier, so that at its convergence the network gives both the optimal feature weight vector as well as the cluster centres and scaling widths.

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Control Systems, Robotics and Automation
Objective Division:Energy
Objective Group:Energy Storage, Distribution and Supply
Objective Field:Energy Systems Analysis
Author:Hoang, TA (Mr Tuan Hoang)
Author:Nguyen, DT (Professor Thong Nguyen)
ID Code:24573
Year Published:2002
Web of Science® Times Cited:4
Deposited By:Engineering
Deposited On:2002-08-01
Last Modified:2003-05-05
Downloads:0

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