Fault diagnosis, of power transformers using SVM/ANN with clonal selection algorithm for features and kernel parameters selection

被引：0

作者：

Cho, Ming-Yuan ^{[1
]}

Lee, Tsair-Fwu ^{[1
]}

Kau, Shih-Wei ^{[1
]}

Shieh, Chin-Shiuh ^{[1
]}

Chou, Chao-Ji ^{[2
]}

机构：

[1] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, Kaohsiung 807, Taiwan

[2] Natl Kaohsiung First Univ Sci & Technol, Kaohsiung 807, Taiwan

来源：

ICICIC 2006: FIRST INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING, INFORMATION AND CONTROL, VOL 1, PROCEEDINGS | 2006年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 effliciency.

引用

页码：26 / +

页数：2

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