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Improving training of radial basis function network for classification of power quality disturbances
journal contributionposted on 2023-05-16, 13:37 authored by Hoang, TA, Nguyen, T
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.
Publication titleElectronics Letters
Department/SchoolSchool of Engineering
PublisherThe Institution of Electrical Engineers Publishing Department
Place of publicationUK