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Implementation of fuzzy inference systems using neural network techniques

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Author
Hudgins, Billy E., Jr.
Date
1992-03
Advisor
Yang, Chyan
Second Reader
Butler, Joe T.
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Abstract
Fuzzy inherence systems work well in many control applications. One drawback, however, is determining membership functions and inference control rules required to implement the system, which are usually supplied by 'experts'. One alternative is to use a neural network-type architecture to implement the fuzzy inference system, and neural network-type training techniques to 'learn' the control parameters needed by the fuzzy inference system. By using a generalized version of a neural network, the rules of the fuzzy inference system can be learned without the assistance of experts.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
URI
http://hdl.handle.net/10945/23919
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  • 1. Thesis and Dissertation Collection, all items

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