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First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)
Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection
Beijing, China
August 30-September 01
ISBN: 0-7695-2616-0
| ASCII Text | x | ||
| Ming-Yuan Cho, Tsair-Fwu Lee, Shih-Wei Kau, Chin-Shiuh Shieh, Chao-Ji Chou, "Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection," Innovative Computing ,Information and Control, International Conference on, vol. 1, pp. 26-30, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06), 2006. | |||
| BibTex | x | ||
| @article{ 10.1109/ICICIC.2006.75, author = {Ming-Yuan Cho and Tsair-Fwu Lee and Shih-Wei Kau and Chin-Shiuh Shieh and Chao-Ji Chou}, title = {Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection}, journal ={Innovative Computing ,Information and Control, International Conference on}, volume = {1}, year = {2006}, isbn = {0-7695-2616-0}, pages = {26-30}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICICIC.2006.75}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Innovative Computing ,Information and Control, International Conference on TI - Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection SN - 0-7695-2616-0 SP26 EP30 A1 - Ming-Yuan Cho, A1 - Tsair-Fwu Lee, A1 - Shih-Wei Kau, A1 - Chin-Shiuh Shieh, A1 - Chao-Ji Chou, PY - 2006 KW - null VL - 1 JA - Innovative Computing ,Information and Control, International Conference on ER - | |||
For the purpose of fault diagnosis of power transformers, a novel approach based on Artificial Neural Network (ANN) and multi-layer Support Vector Machine (SVM) is presented in the paper. The proposed approach is distinguished by features and kernel parameters selection using clonal selection algorithms (CSA). It is capable of filtering out irrelevant input features, leading to improve prediction accuracy. As revealed in the experimental results, the proposed approach outperforms previous ones in both classification accuracy and computational efficiency.
Citation:
Ming-Yuan Cho, Tsair-Fwu Lee, Shih-Wei Kau, Chin-Shiuh Shieh, Chao-Ji Chou, "Fault Diagnosis of Power Transformers Using SVM/ANN with Clonal Selection Algorithm for Features and Kernel Parameters Selection," icicic, vol. 1, pp.26-30, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06), 2006
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