Learning Fuzzy Rules from Data
Document Type
Article
Publication Date
11-1998
Abstract
Fuzzy set theory provides a formal method for modeling complex systems. In classical modeling, system relationships are expressed as mathematical functions. As the systems of interest become more complex, it is increasingly difficult to develop mathematical models directly from knowledge of the system. This is due not only to the complexity of interactions within the system, but perhaps based on an incomplete knowledge of the system operations as well. A fuzzy model uses a set of fuzzy rules to provide a functional approximation of the relationships of the underlying system. The popularity of fuzzy models is attributable to their ability to represent complex, imprecise, or approximate relationships that are difficult to describe in precise mathematical models.
Repository Citation
Hammell, R. J.,
& Sudkamp, T.
(1998). Learning Fuzzy Rules from Data. The Application of Information Technologies to Mission Systems: NATO Research and Technology Organization Proceedings 3, 8.1-8.10.
https://corescholar.libraries.wright.edu/cse/425
DOI
10.14339/RTO-MP-003
