Fuzzy similarity measure is very useful in fuzzy information retrieval. This study presents a new method based on fuzzy-number similarity measure to deal with fuzzy information retrieval problems. The proposed method can obtain the suitable retrieval results for the user because it translate the degree of satisfaction F~ into a linguistic term, and user can easily determine whether the retrieve...

A core tool for granular modeling is the use of linguistic rules, e.g. in fuzzy control approaches. We provide the reader with basic mathematical tools to discuss the behavior of system of such linguistic rules. These mathematical tools range from fuzzy logic and fuzzy set theory, through the consideration of fuzzy relation equations, up to discussions of interpolation strategies and to the use...

There is a significant problem associated with the fuzzy time series. That is a strict increasing and decreasing case. Under the discussion case, fuzzy time series model arise a continuous increasing/decreasing forecasting value. From the illustrative example, we can see that our definition not only define the trend of the fuzzy numbers that represent the linguistic values of the linguistic var...

– Approximate reasoning and fuzzy queries are efficient methods in retrieving information from large databases when precise attributes are unknown or the model itself is vague. We explore such types of reasoning based on the notions of the possibility theory. We suggest an approach towards a Prolog implementation of such queries which takes into account fuzzy linguistic quantification, aggregat...

These notes are concerned with recent developments in multivalued logic, particularly in fuzzy logic and its status as a model for human linguistic reasoning. This first section discusses the status of formal logic and the need for logics of approximate reasoning with vague data. The following sections present a hasic account of fuzzy sets theory; fuzzy logics; Zadeh's model of linguistic hedge...

This paper introduces an approximate fuzzy representation to FuzzyUCS, a Michigan-style Learning Fuzzy-Classifier System that evolves linguistic fuzzy rules, and studies whether the flexibility provided by the approximate representation results in a significant improvement of the accuracy of the models evolved by the system. We test Fuzzy-UCS with both approximate and linguistic representation ...

Because of the complexity of decision-making environment, the uncertainty of fuzziness and the uncertainty of grey maybe coexist in the problems of multi-attribute group decision making. In this paper, we study the problems of multi-attribute group decision making with hybrid grey attribute data (the precise values, interval numbers and linguistic fuzzy variables coexist, and each attribute val...

Ontology has a powerful expressive ability on knowledge. In order to share and deal with the fuzzy knowledge on the semantic web, the linguistic variable ontology is proposed as the basis of the Flu Fuzzy Diagnosis System. After that, Protégé is introduced to build the fuzzy rule base. The paper concludes that linguistic variable ontology-based fuzzy diagnosis system can achieve the function of...

In this paper, we propose accurate linguistic modeling, a methodology to design linguistic models that are accurate to a high degree and may be suitably interpreted. This approach will be based on two main assumptions related to the interpolative reasoning developed by fuzzy rule-based systems: a small change in the structure of the linguistic model based on allowing the linguistic rule to have...

This paper proposes a model for the parametric representation of linguistic hedges in Zadeh’s fuzzy logic. In this model each linguistic truth-value, which is generated from a primary term of the linguistic truth variable, is identified by a real number r depending on the primary term. It is shown that the model yields a method of efficiently computing linguistic truth expressions accompanied w...