Linguistic Truth Values Lattice Implication Algebras

In order to study uncertainty reasoning and automatic reasoning with linguistic terms, in this paper, the set of basic linguistic truth values and the set of modifiers are defined, according to common sense; partially orderings are defined on them. Based on it, a lattice implication algebra model L18 of linguistic terms is built; furthermore, its some basic properties are discussed.

On Normalization of Comprehensive Evaluation System with Complex Uncertain Information

This paper systematically proposes basic requirements on normalization of comprehensive evaluation system with complex uncertain information due to human participation. Four basic academic ideas are as follows: 1) It is necessary to normalize conditions of information acquisition. 2) The effectiveness of comprehensive evaluation depends on the correctness of information acquisition. 3) Any evaluation results can be transformed into linguistic satisfactory degrees. 4) Linguistic values can include a great deal of information. This paper mainly deals with how to select objects to be evaluated, evaluators (panelists) and the methods of information processing, how to construct criteria of evaluation, how to normalize terms of evaluation, the results of evaluation, and the procedure of evaluation.

Some Generalized Single Valued Neutrosophic Linguistic Operators and Their Application to Multiple Attribute Group Decision Making

This paper proposes a group decision making method based on entropy of neutrosophic linguistic sets and generalized single valued neutrosophic linguistic operators. This method is applied to solve the multiple attribute group decision making problems under single valued neutrosophic liguistic environment, in which the attribute weights are completely unknown. First, the attribute weights are obtained by using the entropy of neutrosophic linguistic sets. Then three generalized single valued neutrosophic linguistic operators are introduced, including the generalized single valued neutrosophic linguistic weighted averaging(GSVNLWA) operator, the generalized single valued neutrosophic linguistic ordered weighted averaging(GSVNLOWA) operator and the generalized single valued neutrosophic linguistic hybrid averaging(GSVNLHA) operator, and the GSVNLWA and GSVNLHA operators are used to aggregate information. Furthermore, similarity measure based on single valued neutrosophic linguistic numbers is defined and used to sort the alternatives and obtain the best alternative. Finally,an illustrative example is given to demonstrate the feasibility and effectiveness of the developed method.

Linguistic Valued Association Rules

Association rules discovering and prediction with data mining method are two topics in the field of information processing. In this paper, the records in database are divided into many linguistic values expressed with normal fuzzy numbers by fuzzy c-means algorithm, and a series of linguistic valued association rules are generated. Then the records in database are mapped onto the linguistic values according to largest subject principle, and the support and confidence definitions of linguistic valued association rules are also provided. The discovering and prediction methods of the linguistic valued association rules are discussed through a weather example last.