• Journal of applied mathematics & informatics
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• v.31 no.1_2
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• pp.35-43
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• 2013

In this paper, we introduce the notion of $t$-convex vague sets and study their properties in details.

• 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 the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.603-608
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• 2009

In this paper, a similarity measure between interval-valued vague sets is proposed. In the interval-valued vague sets representation, the upper bound and the lower bound of a vague set are represented as intervals of interval-valued fuzzy set respectively. Proposed method combines the concept of geometric distance and the center-of-gravity point of interval-valued vague set to evaluate the degree of similarity between interval-valued vague sets. We also prove three properties of the proposed similarity measure. It provides a useful way to measure the degree of similarity between interval-valued vague sets.

• 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 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 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 applied mathematics & informatics
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• v.35 no.5_6
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• pp.459-476
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• 2017

Interval valued intuitionistic fuzzy sets (IVIFSs) is widely used to model uncertainty, imprecise, incomplete and vague information. In this paper, newly defined modal operators over an extensional generalized interval valued intuitionistic fuzzy sets ($GIVIFS_Bs$) are proposed. Some of the basic properties of the new operators are discussed and few theorems were proved. The actual contribution in this paper is to discuss ten operators on $GIVIFS_Bs$.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.259-262
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• 2010

Study about fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) were proposed, and analyzed. Unlike fuzzy set, IFSs contains uncertainty named hesistancy, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of ununified entropy definition. By considering different fuzzy entropy definitions, fuzzy entropy is designed and discussed their relation. Similarity measure was also presented and verified its usefulness to evaluate degree of similarity.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.183-188
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• 1996

Fuzzy sets Introduced by Zadeh is a concept which can process, and reson a vague Information using membership functions. The notion of rough sets introduced by Pawlak is based on the ability to classify. reduce. and perform approximation reasoning for the Indiscernible data.A comparison between fuzzy sets and rough sets has been given In Pawlak where it is shown that these concepts are different and can't combine each other. The purpose of this paper Is to Introduce and define the notion of fuzzy-rough sets which joins the membership function of fuzzy sets to the rough sets.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.474-483
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• 2003

A number of studies for corporate bond rating classification problems have demonstrated that artificial intelligence approaches such as Case-based reasoning (CBR) can be alternative methodologies to statistical techniques. CBR is a problem solving technique in that the case specific knowledge of past experience is utilized to find a most similar solution to the new problems. To build a successful CBR system to deal with human information processing, the representation of knowledge of each attribute is an important key factor We propose a hybrid approach of using fuzzy sets that describe the approximate phenomena of the real world because it handles inexact knowledge represented by common linguistic terms in a similar way as human reasoning compared to the other existing techniques. Integration of fuzzy sets with CBR is important to develop effective methods for dealing with vague and incomplete knowledge to statistical represent using membership value of fuzzy sets in CBR.