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Learning and Tuning of Fuzzy Rules

  • Chapter
Fuzzy Systems

Part of the book series: The Springer Handbook Series on Fuzzy Sets ((FSHS,volume 2))

Abstract

The main idea in integrating fuzzy logic systems with learning and tuning techniques is to use the strength of each one collectively in the resulting adaptive fuzzy system. This fusion produces a major group of techniques required for computing with words and soft computing [1, 2, 3]. In this chapter, we discuss a small set of these techniques for generation of fuzzy rules from data and also, a set of techniques for tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rule generation methods. For tuning, we discuss Jang’s ANFIS architecture, Berenji-Khedkar’s GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

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Author information

Authors and Affiliations

  1. Intelligent Inference Systems Corp. Computational Sciences Division, MS: 269-2, NASA Ames Research Center, Mountain View, CA, 94035, USA

    Hamid R. Berenji

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  1. Hamid R. Berenji
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Editor information

Editors and Affiliations

  1. New Mexico State University, USA

    Hung T. Nguyen

  2. Tokyo Institute of Technology, Japan

    Michio Sugeno

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© 1998 Springer Science+Business Media New York

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Berenji, H.R. (1998). Learning and Tuning of Fuzzy Rules. In: Nguyen, H.T., Sugeno, M. (eds) Fuzzy Systems. The Springer Handbook Series on Fuzzy Sets, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5505-6_9

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  • DOI: https://doi.org/10.1007/978-1-4615-5505-6_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7515-9

  • Online ISBN: 978-1-4615-5505-6

  • eBook Packages: Springer Book Archive

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