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
No coin nor oath required. For personal study only.
✦ 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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