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Learning associations between natural groups of input and output with a neurofuzzy structure

✍ Scribed by Özge Uncu; I.B. Türksen


Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
225 KB
Volume
49
Category
Article
ISSN
0921-8890

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✦ Synopsis


A new modeling approach that finds the associations between natural groups of input and output is proposed. In the new method, input and output are clustered separately by means of Fuzzy C-Means (FCM) algorithm. Then, the learning algorithm identifies the fuzzy rules by relating the resulting fuzzy sets in input and output spaces by using a neurofuzzy architecture. A modified version of classical simulated annealing algorithm is used in order to identify the relative weights of system input variables. The proposed approach is applied to a highly nonlinear function and successful result is achieved.


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