| Article |
|---|
| Article name |
An Improved Fuzzy Identification Method Based on Box-Cox Data Transformation |
| Authors |
Fu-Cai Liu .. , shuenwang1968@263.netShu-En Wang .. , shuenwang1968@263.netJin-Mei .. , shuenwang1968@263.net |
| Bibliographic description |
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| Section |
Scientific Research |
| UDK |
519 |
| DOI |
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| Article type |
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| Annotation |
A practical problem in the identification of fuzzy systems from data is the design and the
tuning of the membership functions. Unlike the traditional approaches that utilize original
data patterns to construct the fuzzy model, an approach exploiting both data transformation
techniques and heuristic method is proposed to simplify the modeling procedures. For the
transferred data, firstly, the initial value of fuzzy if-then rules with nonfuzzy singletons in the
consequent parts is generated by the heuristic method. Then, fine-tuning is done by gradient
descent learning algorithm. The proposed method has better approximation accuracy and
better generalization. The method is demonstrated on a DISO problem, using the Box-Cox
transform.
|
| Key words |
fuzzy systems identification; data processing; heuristic method; gradient
descent method; Box-Cox transform. |
| Article information |
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| References |
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| Full article | An Improved Fuzzy Identification Method Based on Box-Cox Data Transformation |
