Article
Article name An Improved Fuzzy Identification Method Based on Box-Cox Data Transformation
Authors Fu-Cai Liu .. , shuenwang1968@263.net
Shu-En Wang .. , shuenwang1968@263.net
Jin-Mei .. , shuenwang1968@263.net
Bibliographic description
Section Scientific Research
UDK 519
DOI
Article type
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.
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Full articleAn Improved Fuzzy Identification Method Based on Box-Cox Data Transformation