Study On Multiple Determining Weights Optimal Models Based On Entropy Theory And Cases Analysis

Comprehensive evaluation and decision analysis methods play important roles in the economic field and in daily life; lots of comprehensive evaluation methods and decision analysis methods have been putting forward by domestic and foreign scholars. The concepts and theoretical bases of rough set theory and fuzzy set theory are introduced based on their features and benefits, then the methods and principles for determining the weight, application characteristics are introduction systematically based on the relevant literatures about the existing method for determining the weight.The certain exploration of determining weights has been carried mainly using rough set theory and fuzzy set theory in the article. The concrete research works are as follows:1 Based on membership function of attribute in rough sets, two methods of using loading matrix of common factors and fuzzy entropy of rough set respectively are put forward to determine the weighted standard decision matrix. Then the method of Topsis is to determine which is optimal in all alternatives.2 The constraints of equivalence relations in classical rough set theory are broken, and the information system based on dominance relations is established, the weights under the concept of algebra and the concept of granularity are carried on the organic integration by the relative entropy optimization model based on dominance relationship, to obtain optimal solution of the attributes in multiple attribute decision making (MADM).3 Group decision making based on intuitionistic fuzzy appraisal matrix was investigated by introducing a new definition of fuzzy entropy based on intuitionistic fuzzy set, which combined with the optimization model of relative entropy.The new definition of fuzzy entropy on intuitionistic fuzzy set balanced between the unknown nature and its uncertainty, which was used to define criteria weights, and the optimal solutions of criteria weights and expert weights were obtianed by introducing the model of relative entropy.4 The membership degree, the nonmembership degree and the hesitaton degree of interval-valued intuitionistic fuzzy set are described by the form of range, which is in line with realistic data. A new fuzzy entropy of interval-valued intuitionistic fuzzy set is introduced, and the fuzzy entropies of the objects under each index of the attribute sets are computed to determine the optimal weights of interval-valued intuitionistic fuzzy sets, subsequently obtaing the evaluation results.