Interval Entropy of Fuzzy Sets and the Application to Fuzzy Multiple Attribute Decision Making

A series of new concepts including interval entropy, interval similarity measure, interval distance measure, and interval inclusion measure of fuzzy sets are introduced. Meanwhile, some theorems and corollaries are proposed to show how these definitions can be deduced from each other. And then, based on interval entropy, a fuzzy multiple attribute decision making (FMADM) model is set up. In this model, interval entropy is used as the weight, by which the evaluation values of all alternatives can be obtained. Then all alternatives with respect to each criterion can be ranked as the order of the evaluation values. At last, a practical example is given to illustrate an application of the developed model and a comparative analysis is made.

FUZZY INFORMATION PROCESSING FOR EXPERT SYSTEMS

This paper investigates the problems incurred when fuzzy values and certainty factors are used in rule-based knowledge representation. It proposes several measures for evaluating the satisfaction degree of fuzzy matching, fuzzy comparison and interval inclusion occurring in the course of inference for such knowledge representation. It introduces an inference method for such knowledge representation. In addition, it suggests a strategy for flexibly using and managing both conventional rules and fuzzy production rules in rule-based systems. Finally a fuzzy expert system shell, called FOPS5, designed to accommodate fuzzy information processing, is presented in consideration of the proposed methods.

Elements of Significance Testing with Equivalence Problems

The paper outlines an approach to the general methodological problem of equivalence assessment which is based on the classical theory of testing statistical hypotheses. Within this frame of reference it is natural to search for decision rules satisfying the same criteria of optimality which are customarily applied in deriving solutions to one- and two-sided testing problems. For three standard situations very frequently encountered in medical applications of statistics, a concise account of such an optimal test for equivalence is presented. It is pointed out that tests based on the well-known principle of confidence interval inclusion are valid in the sense 1 of guaranteeing the prespecified level of significance, but tend to have an unnecessarily low efficiency.