• Title, Summary, Keyword: Fuzzy Set-Fuzzy Systems

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Similarity Analysis Between Fuzzy Set and Crisp Set

  • Park, Hyun-Jeong;Lee, Sang-Hyuk.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.7 no.4
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    • pp.295-300
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    • 2007
  • The similarity analysis for fuzzy set pair or crisp set pair are carried out. The similarity measure that is based on distance measure is derived and proved. The proposed similarity measure is considered with the help of analysis for uncertainty or certainty part of the membership functions. The usefulness of proposed similarity is verified through the computation of similarity between fuzzy set and crisp set or fuzzy set and fuzzy set. Our results are also compared with those of previous similarity measure which is based on fuzzy number.

Generalized Intuitionistic Fuzzy Soft Sets

  • Park, Jin-Han;Kwun, Young-Chel;Hwang, Jin-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.3
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    • pp.389-394
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    • 2011
  • The notion of generalized intuitionistic fuzzy soft set theory is proposed. Our generalized intuitionistic fuzzy soft set theory is a combination of the generalized intuitionistic fuzzy set theory and the soft set theory. In other words, our generalized intuitionistic fuzzy soft set theory is an extension of the intuitionistic fuzzy soft set theory. The complement, "and" and "or" operations are defined on the generalized intuitionistic fuzzy soft sets. Their basic properties for the generalized intuitionistic fuzzy soft sets are also presented and discussed.

Fuzzy r-Generalized Almost Continuity on Fuzzy Generalized Topological Spaces (퍼지 일반화된 위상 공간에서 FUZZY r-GENERALIZED ALMOST CONTINUITY에 관한 연구)

  • Min, Won-Keun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.2
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    • pp.257-261
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    • 2010
  • In this paper, we introduce the concept of fuzzy r-generalized almost continuous mapping and obtain some characterizations of such a mapping. In particular, we investigate characterizations for the fuzzy r-generalized almost continuity by using the concept of fuzzy r-generalized regular open sets.

Intuitionistic Fuzzy Topology and Intuitionistic Fuzzy Preorder

  • Yun, Sang Min;Lee, Seok Jong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.1
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    • pp.79-86
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    • 2015
  • This paper is devoted to finding relationship between intuitionistic fuzzy preorders and intuitionistic fuzzy topologies. For any intuitionistic fuzzy preordered space, an intuitionistic fuzzy topology will be constructed. Conversely, for any intuitionistic fuzzy topological space, we obtain an intuitionistic fuzzy preorder on the set. Moreover, we will show that the family of all intuitionistic fuzzy preorders on an underlying set has a very close link to the family of all intuitionistic fuzzy topologies on the set satisfying some extra condition.

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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    • v.9 no.4
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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.

Fuzzy r-minimal Preopen Sets And Fuzzy r-M Precontinuous Mappings On Fuzzy Minimal Spaces

  • Min, Won-Keun;Kim, Young-Key
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.4
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    • pp.569-573
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    • 2010
  • We introduce the concept of fuzzy r-minimal preopen set on a fuzzy minimal space. We also introduce the concept of fuzzy r-M precontinuous mapping which is a generalization of fuzzy r-M continuous mapping, and investigate characterization of fuzzy r-M precontinuity.

Data Analysis Model using the Fuzzy Property Set (퍼지 속성 집합을 이용한 데이터 분석 모델)

  • 이진호;이전영
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.252-255
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    • 1997
  • In this paper, we will propose the methodology of data analysis using the fuzzy property set model. In real world, the data can be represented with the object. $\theta$. and the property, $\pi$, and its has-property relation, P. Then, the conceptual space can be defined with the chosen properties. Each object has a unique location in the conceptual space. In Fuzzy mode, the fuzzy property, and fuzzy conceptual space can be redefined. To analyze data using the fuzzy property set model, the rough set need to be defined in the fuzzy conceptual space.

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Fuzzy γ-Quasi Open Set and Fuzzy γ-Quasi Continuity

  • Min, Won-Keun
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.3
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    • pp.200-202
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    • 2010
  • In this paper, we introduce the concept of fuzzy ${\gamma}$-quasi open sets which are generalizations of fuzzy ${\gamma}$-open sets, and obtain some basic properties of such fuzzy sets. Also we introduce and study the concepts of fuzzy ${\gamma}$-quasi continuous mapping and fuzzy ${\gamma}$-quasi open(closed) mapping.

Evolutionary Design Methodology of Fuzzy Set-based Polynomial Neural Networks with the Information Granule

  • Roh Seok-Beom;Ahn Tae-Chon;Oh Sung-Kwun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.301-304
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    • 2005
  • In this paper, we propose a new fuzzy set-based polynomial neuron (FSPN) involving the information granule, and new fuzzy-neural networks - Fuzzy Set based Polynomial Neural Networks (FSPNN). We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FSPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. We have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model (node) composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules.

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Selection of data set with fuzzy entropy function (퍼지 엔트로피 함수를 이용한 데이터추출)

  • Lee, Sang-Hyuk;Cheon, Seong-Pyo;Kim, Sung-Shin
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.349-352
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    • 2004
  • In this literature, the selection of data set among the universe set is carried out with the fuzzy entropy function. By the definition of fuzzy entropy, we have proposed the fuzzy entropy function and the proposed fuzzy entropy function is proved through the definition. The proposed fuzzy entropy function calculate the certainty or uncertainty value of data set, hence we can choose the data set that satisfying certain bound or reference. Therefore the reliable data set can be obtained by the proposed fuzzy entropy function. With the simple example we verify that the proposed fuzzy entropy function select reliable data set.

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