• Title/Summary/Keyword: FUZZY 측도

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Fuzzy 측도의 개념과 몇 가지 예에 관한 연구

  • 염승화
    • The Mathematical Education
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    • v.23 no.1
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    • pp.7-11
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    • 1984
  • Fuzzy 측도를 고전적인 방법에 병행해서 T-fuzzy $\sigma$-algebra의 개념을 얻어서 이로부터 Fuzzy 측도를 도입하고 다시 이와 관련된 예를 몇가지 들었다. 이 결과 고전적인 방법과 매우 가까운 Fuzzy 측도를 얻었음을 확인할 수 있었다.

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Fuzzy Measures Defined by the Semi-Normed Fuzzy Integrals (준 노름 퍼지 적분에 의해 정의된 퍼지 측도)

  • Kim, Mi-Hye;Lee, Soon-Seok
    • The Journal of the Korea Contents Association
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    • v.2 no.4
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    • pp.99-103
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    • 2002
  • In this paper, we investigate for how to define a fuzzy measure by using the semi-normed fuzzy integral of a given measurable function with respect to another given fuzzy measure when t-seminorm is continuous. Let (X, F, g) be a fuzzy measure space, h$\in$L$^\circ$(X), and $\top$ be a continuous t-seminorm.. Then the set function $\nu$ defined by $\nu$(A)=$\int _A$h$\top$g for any $A\in$F is a fuzzy measure on (X, F).

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Construction of Fuzzy Entropy and Similarity Measure with Distance Measure (거리 측도를 이용한 퍼지 엔트로피와 유사측도의 구성)

  • Lee Sang-Hyuk;Kim Sung-Shin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.5
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    • pp.521-526
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    • 2005
  • The fuzzy entropy is proposed for measuring of uncertainty with the help of relation between distance measure and similarity measure. The proposed fuzzy entropy is constructed through a distance measure. In this study, Hamming distance measure is employed for a distance measure. Also a similarity measure is constructed through a distance measure for the measure of similarity between fuzzy sets or crisp sets and the proposed fuzzy entropies and similarity measures are proved.

Similarity Measure Construction of the Fuzzy Set for the Reliable Data Selection (신뢰성 있는 정보의 추출을 위한 퍼지집합의 유사측도 구성)

  • Lee Sang-Hyuk
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.9C
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    • pp.854-859
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    • 2005
  • We construct the fuzzy entropy for measuring of uncertainty with the help of relation between distance measure and similarity measure. Proposed fuzzy entropy is constructed through distance measure. In this study, the distance measure is used Hamming distance measure. Also for the measure of similarity between fuzzy sets or crisp sets, we construct similarity measure through distance measure, and the proposed 려zzy entropies and similarity measures are proved.

A New Similarity Measure based on RMF and It s Application to Linguistic Approximation (상대적 소수 함수에 기반을 둔 새로운 유사성 측도와 언어 근사에의 응용)

  • Choe, Dae-Yeong
    • The KIPS Transactions:PartB
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    • v.8B no.5
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    • pp.463-468
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    • 2001
  • We propose a new similarity measure based on relative membership function (RMF). In this paper, the RMF is suggested to represent the relativity between fuzzy subsets easily. Since the shape of the RMF is determined according to the values of its parameters, we can easily represent the relativity between fuzzy subsets by adjusting only the values of its parameters. Hence, we can easily reflect the relativity among individuals or cultural differences when we represent the subjectivity by using the fuzzy subsets. In this case, these parameters may be regarded as feature points for determining the structure of fuzzy subset. In the sequel, the degree of similarity between fuzzy subsets can be quickly computed by using the parameters of the RMF. We use Euclidean distance to compute the degree of similarity between fuzzy subsets represented by the RMF. In the meantime, we present a new linguistic approximation method as an application area of the proposed similarity measure and show its numerical example.

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Gene Screening and Clustering of Yeast Microarray Gene Expression Data (효모 마이크로어레이 유전자 발현 데이터에 대한 유전자 선별 및 군집분석)

  • Lee, Kyung-A;Kim, Tae-Houn;Kim, Jae-Hee
    • The Korean Journal of Applied Statistics
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    • v.24 no.6
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    • pp.1077-1094
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    • 2011
  • We accomplish clustering analyses for yeast cell cycle microarray expression data. To reflect the characteristics of a time-course data, we screen the genes using the test statistics with Fourier coefficients applying a FDR procedure. We compare the results done by model-based clustering, K-means, PAM, SOM, hierarchical Ward method and Fuzzy method with the yeast data. As the validity measure for clustering results, connectivity, Dunn index and silhouette values are computed and compared. A biological interpretation with GO analysis is also included.

The Skeletonization of 2-Dimensional Image for Fuzzy Mathematical Morphology using Defuzzification (비퍼지화를 이용한 퍼지 수학적 형태학의 2차원 영상의 골격화)

  • Park, In-Kue;Lee, Wan-Bum
    • Journal of Digital Contents Society
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    • v.9 no.1
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    • pp.53-60
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    • 2008
  • Based on similarities between fuzzy set theory and mathematical morphology, Grabish proposed a fuzzy morphology based on the Sugeno fuzzy integral. This paper proposes a fuzzy mathematical morphology based on the defuzzification of the fuzzy measure which corresponds to fuzzy integral. Its process makes a fuzzy set used as a measure of the inclusion of each fuzzy measure for subsets. To calculate such an integral a $\lambda$-fuzzy measure is defined which gives every subsets associated with the universe of discourse, a definite non-negative weight. Fast implementable definitions for erosion and dilation based on the fuzzy measure was given. An application for robust skeletonization of two-dimensional objects was presented. Simulation examples showed that the object reconstruction from their skeletal subsets that can be achieved by using the proposed was better than by using the binary mathematical morphology in most cases.

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