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A neural fuzzy controller learning by fuzzy error propagationIn this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.
Document ID
Document Type
Conference Paper
Nauck, Detlef
(Technische Univ. Brunswick, Germany)
Kruse, Rudolf
(Technische Univ. Brunswick, Germany)
Date Acquired
September 6, 2013
Publication Date
December 1, 1992
Publication Information
Publication: NASA. Johnson Space Center, North American Fuzzy Logic Processing Society (NAFIPS 1992), Volume 2
Subject Category
Theoretical Mathematics
Accession Number
Distribution Limits
Work of the US Gov. Public Use Permitted.

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