• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.362-365
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• 2008

In the conventional fuzzy system reliability analysis, the reliabilities of the fuzzy systems and the components of fuzzy systems are represented by real values between zero and one, fuzzy numbers, vague sets, interval valued fuzzy sets, etc. This paper propose a method to represent and analyze the reliabilities of the fuzzy systems based on the internal valued vague sets defined in the universe of discourse [0, 1]. In the interval valued vague sets, the upper bounds and the lower bounds of the conventional vague sets are represented as the intervals, therefore it can allow the reliabilities of a fuzzy system to represent and analyze in a more flexible manner.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.321-331
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• 1997

Measurement of attributes is often highly subjective and imprecise, yet most MADM methods lack provisions for handling imprecise data. Frequently, decision makers must establish a ranking within a finite set of alternatives with respect to multiple attributes which have varying degrees of importance. The problem is more complex if the evaluations of alternatives according to each attribute are not expressed in precise numbers, but rather in fuzzy numbers. Analysis must allow for lack of precision and partial truth. The advantages of a fuzzy approach for MADM are that a decision maker can obtain efficient solutions all at once without trial and error, and that this approach provides better support for judging the interactive improvement of solutions in comparison with o decision making method. The algorithm used in this study is based on the concepts of vague set theory. Linguistic variables and vague values are used to facilitate a decision maker's subjective assessment about attribute weightings and the appropriateness of alternative versus selection attributes in order to obtain final scores which are called vague appropriateness indices. A numerical example is presented to show the practical applicability of this approach.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.927-935
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• 2005

The certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions can be represented by intervals, such as vague numbers between zero and one based on vague sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner[18]. we are also proposed an efficient algorithm to perform vague set reasoning automatically. This vague set reasoning algorithm allows the rule-based systems to perform reasoning in a more flexible and more efficient.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.445-450
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• 2008

In order to analyze the reliabilities of the fuzzy systems, the reliabilities of the components in the fuzzy systems are represented by real values between zero and one, fuzzy numbers, intervals of confidence, vague sets, interval valued fuzzy sets, etc in the conventional researches. In this paper, we propose a method to represent and analyze the reliabilities of the fuzzy systems based on the interval valued vague sets defined in the universe of discourse [0, 1]. In the interval valued vague sets, the upper bounds and the lower bounds of the conventional vague sets[12, 14] are represented as the intervals. Therefore, it can allow the reliabilities of a fuzzy system to represent and analyze in a more flexible manner. Because the proposed method uses the simplified arithmetic operations of the fuzzy triangular numbers rather than the complicated of the fuzzy trapezoidal numbers mentioned by Kumar[14], the execution of the proposed method is faster than the one.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.285-295
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• 2024

Driven by the vague assessment big data, a product service system (PSS) evaluation method is developed based on a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index values of PSS alternatives are solved by the integration of the stakeholders' vague assessment comments presented in the form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solving of PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcome the shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closeness of seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with the highest RE closeness is selected as the best alternative. The results of comparison examples show that the presented model can compensate for the shortcomings of existing traditional methods.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.979-985
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• 2006

In the conventional researches, the reliabilities of the fuzzy system are represented and analyzed by real values between zero and one, fuzzy numbers, intervals of confidence, etc. In this paper, we present a method to represent and analyze the reliabilities of the weighted components of the fuzzy system and the weights reflected on their importance based on vague sets defined in the universe of discourse [0, 1]. The vague set is represented as the interval consisted of the truth-membership functions and the false-membership functions, therefore it can allow the reliabilities and the weights of a fuzzy system to represent in a more flexible manner. The proposed method considers the weights of the weighted components in the fuzzy systems, its reliability analysis is more flexible and effective than the conventional methods.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.52-57
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• 1999

In this paper, the methods which calculate the tuple membership values in a vague model of the fuzzy databases are analyzed A method among them is proposed to offer the effective solutions to the users. Also an information theory is studied to calculate the entropy of the results of a fuzzy query and an algorithm is proposed to control the size of the entropy.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.533-537
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• 2005

A Likert scale is an often used questionnaire format. It requests respondents to specify their level of agreement to each of a list of statements. A typical question using a five-point Likert scale might make a statement. The results shows vague values. We have five-point fuzzy membership function by fuzzy valued three-point for the question and fuzzy hypothesis test the membership function by 95% confidence interval.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.22
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• pp.43-50
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• 1990

In conventional function analysis, the function values are evaluated by experts, which are treated as exact values. For many cases, it is often difficult to evaluate the function values of a certain subject because the criteria of evaluation are very vague. This paper presents a new function evaluation method using fuzzy set. The purpose of the method is to minimize the difference among experts by recognizing an intersection point of membership function as a representative value.

• Journal of KIISE:Software and Applications
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• v.28 no.12
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• pp.930-937
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• 2001

For ranking fuzzy numbers, comparisons between numbers are necessary However, the comparison results can be vague since fuzzy numbers represent vague numeric values. Thus, ranking results of fuzzy numbers which are based on comparisons between fuzzy numbers, could also be vague. This means that there could be several possible ranking sequences of fuzzy numbers. There have been proposed many ranking methods for fuzzy numbers. However, most of them generate only ranking sequence. In this paper, we present a ranking method for fuzzy numbers using the fuzzy satisfaction function, Our method generates several possible ranking sequences of the given fuzzy numbers using the fuzzy satisfaction function.