Learning Algorithms for Fuzzy Inference Systems Using Vector Quantization-Reference-Cited by-同舟云学术

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Learning Algorithms for Fuzzy Inference Systems Using Vector Quantization

Published:2018-12-12 Issue: Volume: Page:
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Container-title:From Natural to Artificial Intelligence - Algorithms and Applications
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Author:

Miyajima Hirofumi,Shigei Noritaka,Miyajima Hiromi

Publisher

IntechOpen

Link

http://www.intechopen.com/download/pdf/62992

Reference20 articles.

1. Gupta MM et al. Static and Dynamic Neural Networks. John Wiley & Sons: IEEE Press; 2004

2. Fukumoto S, Miyajima H, Kishida K, Nagasawa Y. A Destructive Learning Method of Fuzzy Inference Rules. Proc. of IEEE on Fuzzy Systems;1995. pp. 687-694

3. Cordon O. A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems, designing interpretable genetic fuzzy systems. Journal of Approximate Reasoning. 2011;52:894-913

4. Kosko B. Neural Networks and Fuzzy Systems, A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice Hall; 1992

5. Lin C, Lee C. Neural Fuzzy Systems. PTR: Prentice Hall; 1996

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