Topic
Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
Papers published on a yearly basis
Papers
More filters
01 Jan 1995
TL;DR: The position articulated in this article is that probability theory by itself is not sufficient for dealing with uncertainty and imprecision in real-world settings, and it needs an infusion of concepts and techniques drawn from fuzzy logic to enhance its effectiveness.
212 citations
TL;DR: The direct fuzzification of a standard layered, feedforward, neural network where the signals and weights are fuzzy sets is discussed and a fuzzified delta rule is presented for learning.
Abstract: We discuss the direct fuzzification of a standard layered, feedforward, neural network where the signals and weights are fuzzy sets. A fuzzified delta rule is presented for learning. Three applications are given including fuzzy expert systems, fuzzy hierarchical analysis, and fuzzy systems modeling. © 1993 John Wiley & Sons, Inc.
211 citations
TL;DR: Formulas are derived which can calculate the number of input fuzzy sets, output fuzzy sets and fuzzy rules needed in order to satisfy any given approximation accuracy, and it is revealed that the number is minimal when the maximum number of intersection between adjacentinput fuzzy sets is one.
209 citations
TL;DR: A genetic learning process for learning fuzzy control rules from examples is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy controlrules.
209 citations
TL;DR: It is proved that fuzzy systems can represent any linear and multilinear function and explicit expressions of fuzzy systems generated by the MoM defuzzified method are given.
Abstract: This paper establishes the approximation error bounds for various classes of fuzzy systems (i.e., fuzzy systems generated by different inferential and defuzzification methods). Based on these bounds, the approximation accuracy of various classes of fuzzy systems is analyzed and compared. It is seen that the class of fuzzy systems generated by the product inference and the center-average defuzzifier has better approximation accuracy and properties than the class of fuzzy systems generated by the min inference and the center-average defuzzifier, and the class of fuzzy systems defuzzified by the MoM defuzzifier. In addition, it is proved that fuzzy systems can represent any linear and multilinear function and explicit expressions of fuzzy systems generated by the MoM defuzzified method are given.
209 citations