• 제목/요약/키워드: Fuzzy measure

검색결과 453건 처리시간 0.028초

쇼케이적분을 이용한 구간치 퍼지수 상의 거리측도에 관한 성질 (Some algebraic properties and a distance measure for interval-valued fuzzy numbers)

  • 장이채;김원주
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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    • pp.121-124
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    • 2005
  • 퍼지측도와 관련된 폐집합치 쇼케이적분에 대해 장에 의해 연구되어 왔음을 알 수 있다. 본 논문에서는 컴팩트 집합치 함수의 쇼케이적분을 생각하고 이와 관련된 성질들을 조사한다. 특히, 구간치 함수 대신에 컴팩트 집합치 함수를 이용하여 컴팩트 집합치 쇼케이적분의 특성들을 조사한다.

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A Note on Set-Valued Choquet Integrals

  • Hong, Dug-Hun;Kim, Kyung-Tae
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.1041-1044
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    • 2005
  • Recently, Zhang et al.(Fuzzy Sets and Systems 147(2004) 475-485) proved Fatou's lemma and Lebesgue dominated convergence theorem under some conditions of fuzzy measure. In this note, we show that these conditions of fuzzy measure is essential to prove Fatou's lemma and Lebesgue dominated convergence theorem by examples

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Application of fuzzy measure and fuzzy integrals model to evaluation of human interface

  • Sohn, Young-Sun;Onisawa, Takehisa
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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    • pp.787-790
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    • 1997
  • This paper proposes a method which selects essential elements in a human evaluation model using the Choquet integral based on fuzzy measures, and applies the model to the evaluation of human interface. Three kinds of concepts are defined to select essential elements. Increment Degree implies the increment degree from fuzzy measures of composed elements to the fuzzy measure of a combined element. Average of Increment Degree of an element means the relative possibility of superadditivity of the fuzzy measure of each combined element. Necessity Degree means the selection degree of each combined element as a result of the human evaluation. A task experiment, which consists of a static work and two dynamic works, is performed by the use of some human interfaces. In the experiment, (1) a warning sound which gives an attention to subjects, (2) a color vision which can be distinguished easily or not, (3) the size of working area and (4) a response of confirmation that is given from an interface, are considered as human interface elements. Subjects answer the questionnaire after the experiment. From the data of the questionnaire, fuzzy measures are identified and are applied to the proposed model. Effectiveness of the proposed model is confirmed by the comparison of human interface elements extracted from the proposed model and those from the questionnaire.

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쇼케이 거리측도와 응용에 관한 연구 (A study on the Choquet distance measures and their applications)

  • 장이채;김원주
    • 한국지능시스템학회논문지
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    • 제16권5호
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    • pp.550-555
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    • 2006
  • 구간치 퍼지집합은 Gorzalczang(1983)에 의해 처음 제의되었다. 이를 토대로 Wang과 Li는구간치 퍼지수에 관한 연산으로 일반화하여 연구하였다. 최근에 홍(2002)는 왕과 리의 이론을 리만적분에 의해 구간치 퍼지수 상의 거리측도에 관한 연구를 하였다. 우리는 일반측도와 관련된 리만적분 대신에 퍼지측도와 관련된 쇼케이적분을 이용한 구간치 퍼지수 상의 쇼케이 거리측도를 연구하였다(2005). 본 논문에서는 퍼지수치 퍼지수 상의 쇼케이 거리측도를 정의하고 이와 관련된 성질들을 조사하였다.

Calculation of Data Reliability with Entropy for Fuzzy Sets

  • Wang, Hongmei;Lee, Sang-Hyuk
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제9권4호
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    • pp.269-274
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    • 2009
  • Measuring uncertainty for fuzzy sets has been carried out by calculating fuzzy entropy. Fuzzy entropy of fuzzy set is derived with the help of distance measure. The distance proportional value between the fuzzy set and the corresponding crisp set is designed as the fuzzy entropy. The usefulness is verified by proving the proposed entropy. Generally, fuzzy entropy contains the complementary characteristics that the fuzzy entropies of fuzzy set and complementary fuzzy set have the same entropies. Discrepancy that low fuzzy entropy did not guarantee the data certainty was overcome by modifying fuzzy entropy formulation. Obtained fuzzy entropy is analyzed and discussed through simple example.

On entropy for intuitionistic fuzzy sets applying the Euclidean distance

  • Hong, Dug-Hun
    • 한국지능시스템학회논문지
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    • 제12권6호
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    • pp.583-588
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    • 2002
  • Recently, Szmidt and Kacprzyk[Fuzzy Sets and Systems 118(2001) 467-477] proposed a non-probabilistic-type entropy measure for intuitionistic fuzzy sets. Tt is a result of a geometric interpretation of intuitionistic fuzzy sets and uses a ratio of distances between them. They showed that the proposed measure can be defined in terms of the ratio of intuitionistic fuzzy cardinalities: of $F\bigcapF^c and F\bigcupF^c$, while applying the Hamming distances. In this note, while applying the Euclidean distances, it is also shown that the proposed measure can be defined in terms of the ratio of some function of intuitionistic fuzzy cardinalities: of $F\bigcapF^c and F\bigcupF^c$.

On entropy for intuitionistic fuzzy sets applying the Euclidean distance

  • Hong, Dug-Hun
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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    • pp.13-16
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    • 2002
  • Recently, Szmidt and Kacprzyk[Fuzzy Sets and Systems 118(2001) 467-477] Proposed a non-probabilistic-type entropy measure for intuitionistic fuzzy sets. It is a result of a geometric interpretation of intuitionistic fuzzy sets and uses a ratio of distances between them. They showed that the proposed measure can be defined in terms of the ratio of intuitionistic fuzzy cardinalities: of F∩F$\^$c/ and F∪F$\^$c/, while applying the Hamming distances. In this note, while applying the Euclidean distances, it is also shown that the proposed measure can be defined in terms of the ratio of some function of intuitionistic fuzzy cardinalities: of F∩F$\^$c/ and F∪F$\^$c/.

Fuzzy Entropy Construction based on Similarity Measure

  • 박현정;양인석;류수록;이상혁
    • 한국지능시스템학회논문지
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    • 제18권2호
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    • pp.257-261
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    • 2008
  • In this Paper we derived fuzzy entropy that is based on similarity measure. Similarity measure represents the degree of similarity between two informations, those informations characteristics are not important. First we construct similarity measure between two informations, and derived entropy functions with obtained similarity measure. Obtained entropy is verified with proof. With the help of one-to-one similarity is also obtained through distance measure, this similarity measure is also proved in our paper.