Transition network grammars for natural language analysis
Communications of the ACM
The problem of linguistic approximation in system analysis
The problem of linguistic approximation in system analysis
Fuzzy trust evaluation and credibility development in multi-agent systems
Applied Soft Computing
Uncertainty measures for interval type-2 fuzzy sets
Information Sciences: an International Journal
A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets
Information Sciences: an International Journal
A Vector Similarity Measure for Type-1 Fuzzy Sets
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Uncertainty measures for general type-2 fuzzy sets
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Uncertainty measures for general Type-2 fuzzy sets
Information Sciences: an International Journal
Information Sciences: an International Journal
| Hi-index | 0.00 |
Fuzzy sets theory and fuzzy logic constitute the basis for the linguistic approach. Under this approach, variables can assume linguistic values. Each linguistic value is characterized by a label and a meaning. The label is a sentence of a language. The meaning is a fuzzy subset of a universe of discourse. Models, based on this approach, can be constructed to simulate approximate reasoning. The implementation of these models presents two major problems, namely how to associate a label to an unlabelled fuzzy set on the basis of semantic similarity (linguistic approximation) and how to perform arithmetic operations with fuzzy numbers. For each problem a solution is proposed. Two illustrative applications are discussed.