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Refining Linear Fuzzy Rules by Reinforcement LearningLinear fuzzy rules are increasingly being used in the development of fuzzy logic systems. Radial basis functions have also been used in the antecedents of the rules for clustering in product space which can automatically generate a set of linear fuzzy rules from an input/output data set. Manual methods are usually used in refining these rules. This paper presents a method for refining the parameters of these rules using reinforcement learning which can be applied in domains where supervised input-output data is not available and reinforcements are received only after a long sequence of actions. This is shown for a generalization of radial basis functions. The formation of fuzzy rules from data and their automatic refinement is an important step in closing the gap between the application of reinforcement learning methods in the domains where only some limited input-output data is available.
Document ID
20020038872
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Berenji, Hamid R.
(RECOM Technologies, Inc. Moffett Field, CA United States)
Khedkar, Pratap S.
(General Electric Co. Schenectady, NY United States)
Malkani, Anil
(RECOM Technologies, Inc. Moffett Field, CA United States)
Date Acquired
August 20, 2013
Publication Date
January 1, 1996
Subject Category
Numerical Analysis
Meeting Information
Meeting: Fifth IEEE International Conference on Fuzzy Systems
Location: New Orleans, LA
Country: United States
Start Date: September 8, 1996
End Date: September 11, 1996
Sponsors: Institute of Electrical and Electronics Engineers
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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