• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.85-89
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• 2006

구간값 퍼지집합은 일반적인 퍼지집합보다 언어적인 의사결정 절차에서 매핑의 정확성과 계산의 효율성이 뛰어나고, 규칙의 가중치는 패턴 분류문제에서 분류 경계를 효율적으로 조정할 수 있다는 장점을 가지고 있다. 따라서 본 논문에서는 퍼지규칙 기반 분류방법을 구간값 퍼지규칙 기반 분류방법으로 확장하고 규칙의 가중치를 고려한 분류방법을 제안한다. 모의실험에서는 일반 퍼지집합에서 규칙 가중치를 고려한 분류방법과 구간값 퍼지집합에서 규칙 가중치를 고려한 분류방법을 비교하였다.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.783-790
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• 2002

This paper proposes a fuzzy Pr/T net representation of interval-valued fuzzy set reasoning, where fuzzy production rules are used for knowledge representation, and the belief of fuzzy production rules are represented by interval-valued fuzzy sets. The presented interval-valued fuzzy reasoning algorithm is much closer to human intuition and reasoning than other methods because this algorithm uses the proper belief evaluation functions according to fuzzy concepts in fuzzy production rules.

• Journal of Internet Computing and Services
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• v.4 no.4
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• pp.45-51
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• 2003

In general, the values for attribute appearing in fuzzy object-oriented data models are represented by the fuzzy sets. If it can allow the attribute values in the fuzzy object-oriented data models to be represented by the interval-valued fuzzy sets, then it can allow the fuzzy object-oriented data models to represent the attribute values in more flexible manner. The attribute values of frames appearing in the inheritance structure of the fuzzy object-oriented data models are calculated by a prloritized conjunction operation using interval-valued fuzzy sets. This approach can be applied to knowledge and information processing in which degree of membership is represented as not the conventional fuzzy sets but the interval-valued fuzzy sets.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.559-566
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• 2004

In general, 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 to be represented by interval -valued fuzzy sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner. This paper presents fuzzy Petri nets and proposes an interval-valued fuzzy backward reasoning algorithm for rule-based systems based on fuzzy Petri nets Fuzzy Petri nets model the fuzzy production rules in the knowledge base of a rule-based system, where the certainty factors of the fuzzy propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by interval-valued fuzzy sets. The algorithm we proposed generates the backward reasoning path from the goal node to the initial nodes and then evaluates the certainty factor of the goal node. The proposed interval-valued fuzzy backward reasoning algorithm can allow the rule-based systems to perform fuzzy backward reasoning in a more flexible and human-like manner.

• 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 the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.443-450
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• 2007

In this paper, we propose a novel threshold selection method based on statistical information on gray-levels of given images and interval-valued fuzzy sets. In the proposed threshold selection method, the interval-valued fuzzy set is used to represent more definitely the relationship between a pixel and its belonging region, that is, the object and the background. Also the statistical information on gray-level is used to determine the rules and partitions of interval-valued fuzzy sets. To show the validity of the proposed method, we compared the performance of the proposed with those of conventional methods such as Otsu's method, Huang and Wang's method applied to 5 test images with various types of histograms.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.625-631
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• 2004

In general, 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 to be represented by interval-valued fuzzy sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner(15). This paper presents a fuzzy Petri nets and proposes an interval-valued fuzzy reasoning algorithm for rule-based systems based on fuzzy Petri nets. Fuzzy Petri nets model the fuzzy production rules in the knowledge base of a rule-based system, where the certainty factors of the fuzzy Propositions appearing in the furry production rules and the certainty factors of the rules are represented by interval-valued fuzzy sets. The proposed interval-valued fuzzy set reasoning algorithm can allow the rule-based systems to perform fuzzy reasoning in a more flexible manner.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.44-48
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• 2000

퍼지 집합은 경계가 애매한 집단, 어떤 제약에 대한 만족정도가 애매한 개체들의 모임, 또는 애매한 개념을 소속정도를 이용하여 표현한다. 퍼지 집합에서는 자신의 나타내는 개념이나 제약에 대해서 무관한 개체나 상반되는 개체에 대해서도 소속정도 값으로 0을 부여한다. 응용에 따라서는 집합이 나타내는 개념이나 제약에 대해서 무관한 것과 상반되는 것을 구별하여 표현하는 것이 유용한 경우도 있다. 이 논문에서는 퍼지 집합에서 소속정도값 0을 갖는 무관한 원소들과 상반되는 원소들을 구별하여 표현하기 위해 소속 정도값의 영역을 구간 [-1, 1]로 확장한 바이폴라 퍼지집합이라는 확장된 퍼지 집합을 소개한다. 한편, 바이폴라 퍼지 집합에 대한 집합연산, 퍼지정도 척도, 관계, 추론 등의 연산에 대해서도 소개한다.

• 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 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.