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Improving training of radial basis function network for classification of power quality disturbances
Citation
Hoang, TA and Nguyen, T, 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]
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: | Control engineering, mechatronics and robotics |
| Research Field: | Field robotics |
| Objective Division: | Energy |
| Objective Group: | Energy storage, distribution and supply |
| Objective Field: | Energy systems and analysis |
| UTAS Author: | Hoang, TA (Mr Tuan Hoang) |
| UTAS Author: | Nguyen, T (Professor Thong Nguyen) |
| ID Code: | 24573 |
| Year Published: | 2002 |
| Web of Science® Times Cited: | 5 |
| Deposited By: | Engineering |
| Deposited On: | 2002-08-01 |
| Last Modified: | 2003-05-05 |
| Downloads: | 0 |
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