Shi-Jay Chen

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7].

A NEW METHOD FOR HANDLING MULTICRITERIA FUZZY DECISION-MAKING PROBLEMS USING FN-IOWA OPERATORS

We use fuzzy numbers to extend the traditional induced ordered weight averaging (IOWA) operator to present the fuzzy-number IOWA (FN-IOWA) operator, wherein fuzzy numbers are used to describe the argument values and the weights of the FN-IOWA operator, and the aggregation results are obtained by using fuzzy-number arithmetic operations. We also present a new method for ranking fuzzy numbers. Based on the proposed FN-IOWA operator and the proposed ranking method of fuzzy numbers, we present a new algorithm to deal with multicriteria fuzzy decision-making problems. The proposed algorithm can deal with multicriteria fuzzy decision-making problems in a more intelligent and more flexible manner.

Fuzzy risk analysis based on measures of similarity between interval-valued fuzzy numbers

In this paper, we present a new method for handling fuzzy risk analysis problems based on measures of similarity between interval-valued fuzzy numbers. First, we propose a similarity measure to calculate the degree of similarity between interval-valued fuzzy numbers. The proposed similarity measure uses the concept of geometry to calculate the center-of-gravity (COG) points of the lower fuzzy numbers and the upper fuzzy numbers of interval-valued fuzzy numbers, respectively, to calculate the degree of similarity between interval-valued fuzzy numbers. We also prove some properties of the proposed similarity measure. Then, we use the proposed similarity measure for interval-valued fuzzy numbers for handling fuzzy risk analysis problems. The proposed method is more flexible and more intelligent than the methods presented in [S.J. Chen, S.M. Chen, Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers, IEEE Transactions on Fuzzy Systems 11 (1) (2003) 45-56; S.M. Chen, Evaluating the rate of aggregative risk in software development using fuzzy set theory, Cybernetics and Systems 30 (1) (1999) 57-75; S.M. Chen, New methods for subjective mental workload assessment and fuzzy risk analysis, Cybernetics and Systems 27 (5) (1996) 449-472; H.M. Lee, Applying fuzzy set theory to evaluate the rate of aggregative risk in software development, Fuzzy Sets and Systems 79 (3) (1996) 323-336; K.J. Schmucker, Fuzzy Sets, Natural Language Computations, and Risk Analysis, Computer Science Press, MD (1984)] due to the fact that it uses interval-valued fuzzy numbers rather than fuzzy numbers or generalized fuzzy numbers for handling fuzzy risk analysis problems. It provides us with a useful way for handling fuzzy risk analysis problems.

A new method to measure the similarity between fuzzy numbers

In this paper, we present a new method to measure the degree of similarity between fuzzy numbers. The proposed method uses the concept of geometry to calculate the center-of-gravity (COG) points of trapezoidal or triangular fuzzy numbers and then to calculate the degree of similarity between fuzzy numbers. We also prove some properties of the proposed similarity measure and use an example to compare the proposed method with the existing methods. The proposed similarity measure can overcome the drawbacks of the existing methods.

A Prioritized Information Fusion Method for Handling Fuzzy Decision-Making Problems

Although Yager has presented a prioritized operator for fuzzy subsets, called the non-monotonic operator, it can not be used to deal with multi-criteria fuzzy decision-making problems when generalized fuzzy numbers are used to represent the evaluating values of criteria. In this paper, we present a prioritized information fusion algorithm based on the similarity measure of generalized fuzzy numbers. The proposed prioritized information fusion algorithm has the following advantages: (1) It can handle prioritized multi-criteria fuzzy decision-making problems in a more flexible manner due to the fact that it allows the evaluating values of criteria to be represented by generalized fuzzy numbers or crisp values between zero and one, and (2) it can deal with prioritized information filtering problems based on generalized fuzzy numbers.

A New Similarity Measure of Generalized Fuzzy Numbers Based on Geometric-mean Averaging Operator

This study presents a new similarity measure based on the geometric-mean averaging operator to handle the similarity measure problems of generalized fuzzy numbers. Some properties of the proposed similarity measure are demonstrated, and 26 sets of generalized fuzzy numbers are used to compare the proposed method with existing similarity measures. Comparison results indicate that the proposed similarity measure is better than existing methods.

Fuzzy Information Retrieval Based On A New Similarity Measure Of Generalized Fuzzy Numbers

Abstract This study presents a new similarity measure based on the geometric-mean averaging operator to handle the similarity measure problems of generalized fuzzy numbers. Some properties of the proposed similarity measure are demonstrated, and 26 sets of generalized fuzzy numbers are used to compare the proposed method with existing similarity measures. Comparison results indicate that the proposed similarity measure is better than existing methods. Finally, the proposed similarity measure is applied to propose an algorithm for handling fuzzy-number information retrieval problems.

Similarity Measure between Generalized Fuzzy Numbers Using Quadratic-Mean Operator

This presents a novel similarity measure that is based on the quadratic-mean operator to solve similarity measurement problems that involve generalized fuzzy numbers. Some properties of the proposed similarity measure are demonstrated, and 36 sets of generalized fuzzy numbers are adopted to compare the proposed method with existing similarity measurement methods. The results of the comparison show that the proposed similarity measure is better than existing methods.

A new method for fuzzy query processing of document retrieval based on extended fuzzy concept networks

This study proposes a new mechanism based on extended fuzzy concept networks for fuzzy query processing of document retrieval and we use relevance matrix and relation matrix to model extended fuzzy concept networks. This mechanism combines the document descriptor relevance matrix defined by the expert with the users query descriptor based on different weights for obtaining a matrix called satisfaction matrix. This mechanism uses AND operator of the quadratic-mean averaging operators to calculate all the components in each row of the satisfaction matrix. Finally, ranking the degrees of satisfaction of each satisfaction matrix obtains documents more suitable for the users needs.

A new prioritized information fusion method for handling fuzzy information retrieval problems

In this paper, we present a new prioritized information fusion method for handling fuzzy information retrieval problems. We also present a new center-of-gravity method for ranking generalized fuzzy numbers. Furthermore, we also extend the proposed prioritized information fusion method for handling fuzzy information retrieval problems in the generalized fuzzy number environment, where generalized fuzzy numbers are used to represent the degrees of strength with which documents satisfy particular criteria.