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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Meng-Hui Wang Yi-Feng Tseng Hong-Cheng Chen Kuei-Hsiang Chao |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. Eng., Nat. Chin-Yi Univ. of Technol., Taiping (Meng-Hui Wang; Yi-Feng Tseng; Hong-Cheng Chen; Kuei-Hsiang Chao) |
| Abstract | This paper presents a novel classified method that is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustered problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in clustering process. In order to improve this defect, the paper uses the EGA to find the best parameter of classical domain. Through the simulations, we prove that this new method can eliminate try and error adjustment of modeling parameters and increase accuracy of the classification. |
| Starting Page | 601 |
| Ending Page | 601 |
| File Size | 269784 |
| Page Count | 1 |
| File Format | |
| ISBN | 9780769531618 |
| DOI | 10.1109/ICICIC.2008.270 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fault diagnosis Training Accuracy Artificial neural networks Oil insulation Power transformers Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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