Topic
Fuzzy number
About: Fuzzy number is a research topic. Over the lifetime, 35606 publications have been published within this topic receiving 972544 citations.
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TL;DR: A unified presentation of classical clustering algorithms is proposed both for the hard and fuzzy pattern classification problems, and two coefficients that measure the “degree of non-fuzziness” of the partition are proposed.
209 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
TL;DR: A forecasting framework based on the fuzzy multi-criteria decision making (FMCDM) approach is developed to help organizations build awareness of the critical influential factors on the success of knowledge management (KM) implementation, measure the success possibility ofknowledge management projects, as well as identify the necessary actions prior to embarking on conducting knowledge management.
209 citations