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Learning and Tuning of Fuzzy RulesIn this chapter, we review some of the current techniques for learning and 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 rules 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.
Document ID
20020051103
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Berenji, Hamid R.
(NASA Ames Research Center Moffett Field, CA United States)
Date Acquired
August 20, 2013
Publication Date
January 1, 1997
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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