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http://localhost:8080/xmlui/handle/123456789/6284Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Al Mashhadany, Yousif | - |
| dc.date.accessioned | 2022-10-24T19:55:53Z | - |
| dc.date.available | 2022-10-24T19:55:53Z | - |
| dc.date.issued | 2014 | - |
| dc.identifier.issn | 1947-3818 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/6284 | - |
| dc.description.abstract | The proposed controller incorporates FL (fuzzy logic) algorithm with ANN (artificial neural network). ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Energy and Power Engineering 8 (2014) pp: 729-734 | en_US |
| dc.subject | ANFIS controller | en_US |
| dc.subject | power system | en_US |
| dc.subject | high performance | en_US |
| dc.subject | learning algorithm | en_US |
| dc.title | High-Performance of Power System Based upon ANFIS (Adaptive Neuro-Fuzzy Inference System) Controller | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | الهندسة الكهربائية | |
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