Implementation of fuzzy inference systems using neural network techniques
Hudgins, Billy E., Jr.
Butler, Joe T.
MetadataShow full item record
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.
RightsThis 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.
Showing items related by title, author, creator and subject.
ENHANCED MULTI-LABEL CLASSIFICATION OF HETEROGENEOUS UNDERWATER SOUNDSCAPES BY CONVOLUTIONAL NEURAL NETWORKS USING BAYESIAN DEEP LEARNING Beckler, Brandon M. (Monterey, California. Naval Postgraduate School, 2021-09);The classification of underwater soundscapes is a challenging task for humans as well as machine learning systems. This is largely due to the heterogenous nature of these soundscapes, especially in coastal zones close ...
Rowe, Neil C. (Monterey, California. Naval Postgraduate School, 1989-02);Indirect logical inferences can provide a significant security threat to information processing systems, but they have not been much studied. Classification of data can reduce the threat, but classification decisions ...
Bull, Bruce James (Monterey, California. Naval Postgraduate School, 1994-03);Interactive programming environment for language offer many advantages over traditional batch-oriented ones, such as immediate static analysis. One form of analysis is type checking, yet type checking in this setting for ...