Fundamentals of fuzzy sets

By Didier Dubois, Henri Prade, Lotfi A. Zadeh. 2000

652 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/04a3fd5ccc1c2dc9b25d81469fc37665cf35e7e7

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In Artificial Intelligence

Citations in the corpus

Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge (2003, background): “…case, when some values appear more plausible than others, the natural extension of an interval is a fuzzy interval (Dubois and Prade, 1980, 1988; Dubois et al., 2000), that is, a possibility distribution representing more or less plausible values, viewed as a nested family of confidence…”
Application of fuzzy inference system for prediction of rock fragmentation induced by blasting (2015, methodology): “In this method, any type of information ranging from intervalvalued numerical data to linguistic expressions can be processed (Dubois and Prade 2000).”
Monitoring Inequality among Social Groups: A Methodology Combining Fuzzy Set Theory and Principal Component Analysis1 (2008, neutral): “Three different fuzzy set aggregators have been considered: strong intersection, weak intersection, and arithmetic average (for a summary on other operators, see Chiappero Martinetti, 2006; Dubois and Prade, 2000 ).”
Linear programming with fuzzy parameters: An interactive method resolution (2007, methodology): “There are several methods to do this (see i.e. Dubois et al., 2000).”
Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis (2006, methodology): “To do this, we use established results from fuzzy set theory for the intersection and union of fuzzy sets (e.g., [ 29 ]).”
On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process (2007, methodology): “There are many different ways to construct fuzzy numbers from raw information (see [2,4,5, 6 ,8,10] for more details on fuzzy numbers and their construction).”