Implementation of fuzzy inference systems using neural network techniques
Loading...
Authors
Hudgins, Billy E., Jr.
Subjects
Fuzzy inference
Neural networks
Adaptive training
Gradient descent
Membership functions
Neural networks
Adaptive training
Gradient descent
Membership functions
Advisors
Yang, Chyan
Date of Issue
1992-03
Date
March 1992
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
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.
Type
Thesis
Description
Series/Report No
Department
Department of Electrical and Computer Engineering
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
37 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
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.