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Artykuły w czasopismach na temat „Fuzzy Measures”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 20 lutego 2023

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1

Congxin Wu, Deli Zhang, Caimei Guo i Cong Wu. "Fuzzy number fuzzy measures and fuzzy integrals. (I). Fuzzy integrals of functions with respect to fuzzy number fuzzy measures". Fuzzy Sets and Systems 98, nr 3 (wrzesień 1998): 355–60. http://dx.doi.org/10.1016/s0165-0114(96)00394-6.

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2

Squillante, Massimo, i Aldo G. S. Ventre. "Generating fuzzy measures". Journal of Mathematical Analysis and Applications 165, nr 2 (kwiecień 1992): 550–55. http://dx.doi.org/10.1016/0022-247x(92)90058-l.

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3

Glass, David H. "Fuzzy confirmation measures". Fuzzy Sets and Systems 159, nr 4 (luty 2008): 475–90. http://dx.doi.org/10.1016/j.fss.2007.07.018.

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4

Cutello, Vincenzo, i Javier Montero. "Fuzzy rationality measures". Fuzzy Sets and Systems 62, nr 1 (luty 1994): 39–54. http://dx.doi.org/10.1016/0165-0114(94)90071-x.

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5

Sancho-Royo, A., i J. L. Verdegay. "Fuzzy coherence measures". International Journal of Intelligent Systems 20, nr 1 (styczeń 2005): 1–11. http://dx.doi.org/10.1002/int.20050.

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6

de Glas, Michel. "Fuzzy σ-fields and fuzzy measures". Journal of Mathematical Analysis and Applications 124, nr 1 (maj 1987): 281–89. http://dx.doi.org/10.1016/0022-247x(87)90039-4.

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7

Caimei Guo, Deli Zhang i Congxin Wu. "Fuzzy-valued fuzzy measures and generalized fuzzy integrals". Fuzzy Sets and Systems 97, nr 2 (lipiec 1998): 255–60. http://dx.doi.org/10.1016/s0165-0114(96)00276-x.

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8

Maturo, Antonio. "Fuzzy measures and coherent join measures". International Journal of Intelligent Systems 26, nr 12 (11.10.2011): 1196–205. http://dx.doi.org/10.1002/int.20512.

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9

Wu, Congxin, Deli Zhang, Bokan Zhang i Caimei Guo. "Fuzzy number fuzzy measures and fuzzy integrals. (II). Fuzzy integrals of fuzzy-valued functions with respect to fuzzy number fuzzy measures on fuzzy sets". Fuzzy Sets and Systems 101, nr 1 (styczeń 1999): 137–41. http://dx.doi.org/10.1016/s0165-0114(97)00041-9.

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10

CALVO, T., J. MARTIN, G. MAYOR i J. TORRENS. "BALANCED DISCRETE FUZZY MEASURES". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, nr 06 (grudzień 2000): 665–76. http://dx.doi.org/10.1142/s0218488500000484.

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First a balancing property on a fuzzy measure is introduced. After that, the conditions of when an additive fuzzy measure is balanced are given and similar results are presented for 0-1 and S–decomposable fuzzy measures where S is a continuous t–conorm. Moreover, the concept of distance between two additive fuzzy measures is presented and some results related to the distance and the balancing property are developed.

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11

Honda, Aoi, i Yoshiaki Okazaki. "Identification of Fuzzy Measures with Distorted Probability Measures". Journal of Advanced Computational Intelligence and Intelligent Informatics 9, nr 5 (20.09.2005): 467–76. http://dx.doi.org/10.20965/jaciii.2005.p0467.

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We consider the identification of fuzzy measures using a class of distorted probabilities – a scale transformation of probabilities. A fuzzy measure, which is a nonadditive set function with a high degree of freedom, enables us to express complicated interactions among evaluative items. Because of the high degree of freedom, however, it is difficult to identify all of the values μ(A) for every event A from known data μ(B), B ∈ A, where A is generally a small subclass of events. In this paper, we classify fuzzy measures by introducing “type,” and propose an identifying fuzzy measures using a classified class of distorted probabilities.

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12

Phong, Pham Hong, i Vu Thi Hue. "On Integration Linguistic Factors to Fuzzy Similarity Measures and Intuitionistic Fuzzy Similarity Measures". International Journal of Synthetic Emotions 10, nr 1 (styczeń 2019): 1–37. http://dx.doi.org/10.4018/ijse.2019010101.

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The article is concerned with integrating linguistic elements into fuzzy similarity measures and intuitionistic fuzzy similarity measure. Some new concepts are proposed: a fuzzy linguistic value (FLv), a fuzzy linguistic vector (FLV), an intuitionistic fuzzy linguistic vector (ILV) and similarity measures. The proposed measures are used to build classification algorithms. As predicted theoretically, experiments show that with the same type of similarity measures, the linguistic-aggregated similarity measures produce better results in classification problems.

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13

Suzuki, Hisakichi. "Atoms of fuzzy measures and fuzzy integrals". Fuzzy Sets and Systems 41, nr 3 (czerwiec 1991): 329–42. http://dx.doi.org/10.1016/0165-0114(91)90136-e.

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14

SIMS, JOHN R., i WANG ZHENYUAN. "FUZZY MEASURES AND FUZZY INTEGRALS: AN OVERVIEW". International Journal of General Systems 17, nr 2-3 (czerwiec 1990): 157–89. http://dx.doi.org/10.1080/03081079008935106.

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15

Yager, Ronald R. "On Viewing Fuzzy Measures as Fuzzy Subsets". IEEE Transactions on Fuzzy Systems 24, nr 4 (1.08.2016): 811–18. http://dx.doi.org/10.1109/tfuzz.2015.2486808.

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16

Suzuki, Hisakichi. "On fuzzy measures defined by fuzzy integrals". Journal of Mathematical Analysis and Applications 132, nr 1 (maj 1988): 87–101. http://dx.doi.org/10.1016/0022-247x(88)90045-5.

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17

MIRANDA, PEDRO, MICHEL GRABISCH i PEDRO GIL. "p-SYMMETRIC FUZZY MEASURES". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, supp01 (grudzień 2002): 105–23. http://dx.doi.org/10.1142/s0218488502001867.

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In this paper we propose a generalization of the concept of symmetric fuzzy measure based in a decomposition of the universal set in what we have called subsets of indifference. Some properties of these are studied, as well as their Choquet integral. Finally, a degree of interaction between the subsets of indifference is defined.

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18

Mesiar, Radko, i Endre Pap. "Fuzzy measures and integrals". Fuzzy Sets and Systems 102, nr 3 (marzec 1999): 361. http://dx.doi.org/10.1016/s0165-0114(98)00210-3.

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19

Mesiar, Radko. "Fuzzy measures and integrals". Fuzzy Sets and Systems 156, nr 3 (grudzień 2005): 365–70. http://dx.doi.org/10.1016/j.fss.2005.05.033.

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20

Lamata, M. T., i S. Moral. "Classification of fuzzy measures". Fuzzy Sets and Systems 33, nr 2 (listopad 1989): 243–53. http://dx.doi.org/10.1016/0165-0114(89)90245-5.

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21

Wu, Congxin, i Xuekun Ren. "Fuzzy measures and discreteness". Discrete Mathematics 308, nr 21 (listopad 2008): 4839–45. http://dx.doi.org/10.1016/j.disc.2007.09.003.

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22

Squillante, Massimo, i Aldo G. S. Ventre. "Fuzzy measures and convergence". Fuzzy Sets and Systems 25, nr 2 (luty 1988): 251–57. http://dx.doi.org/10.1016/0165-0114(88)90191-1.

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23

Xu, Zeshui. "Fuzzy ordered distance measures". Fuzzy Optimization and Decision Making 11, nr 1 (17.11.2011): 73–97. http://dx.doi.org/10.1007/s10700-011-9113-6.

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24

Mesiar, Radko. "Possibility measures, integration and fuzzy possibility measures". Fuzzy Sets and Systems 92, nr 2 (grudzień 1997): 191–96. http://dx.doi.org/10.1016/s0165-0114(97)00169-3.

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25

Santos, Helida, Inés Couso, Benjamin Bedregal, Zdenko Takáč, Mária Minárová, Alfredo Asiaın, Edurne Barrenechea i Humberto Bustince. "Similarity measures, penalty functions, and fuzzy entropy from new fuzzy subsethood measures". International Journal of Intelligent Systems 34, nr 6 (29.01.2019): 1281–302. http://dx.doi.org/10.1002/int.22096.

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26

Haryati, Anisa Eka, i Sugiyarto Surono. "COMPARATIVE STUDY OF DISTANCE MEASURES ON FUZZY SUBTRACTIVE CLUSTERING". MEDIA STATISTIKA 14, nr 2 (12.12.2021): 137–45. http://dx.doi.org/10.14710/medstat.14.2.137-145.

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Clustering is a data analysis process which applied to classify the unlabeled data. Fuzzy clustering is a clustering method based on membership value which enclosing set of fuzzy as a measurement base for classification process. Fuzzy Subtractive Clustering (FSC) is included in one of fuzzy clustering method. This research applies Hamming distance and combined Minkowski Chebysev distance as a distance parameter in Fuzzy Subtractive Clustering. The objective of this research is to compare the output quality of the cluster from Fuzzy Subtractive Clustering by using Hamming distance and combine Minkowski Chebysev distance. The comparison of the two distances aims to see how well the clusters are produced from two different distances. The data used is data on hypertension. The variables used are age, gender, systolic pressure, diastolic pressure, and body weight. This research shows that the Partition Coefficient value resulted on Fuzzy Subtractive Clustering by applying combined Minkowski Chebysev distance is higher than the application of Hamming distance. Based on this, it can be concluded that in this study the quality of the cluster output using the combined Minkowski Chebysev distance is better.

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27

Mishra, Arunodaya Raj, Divya Jain i D. S. Hooda. "On fuzzy distance and induced fuzzy information measures". Journal of Information and Optimization Sciences 37, nr 2 (3.03.2016): 193–211. http://dx.doi.org/10.1080/02522667.2015.1103034.

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28

Liu, Shiang-Tai, i Chiang Kao. "Fuzzy measures for correlation coefficient of fuzzy numbers". Fuzzy Sets and Systems 128, nr 2 (czerwiec 2002): 267–75. http://dx.doi.org/10.1016/s0165-0114(01)00199-3.

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29

Liu, Shiang-Tai. "Fuzzy measures for profit maximization with fuzzy parameters". Journal of Computational and Applied Mathematics 236, nr 6 (październik 2011): 1333–42. http://dx.doi.org/10.1016/j.cam.2011.08.019.

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30

Congxin, Wu, i Sun Bo. "Pseudo-atoms of fuzzy and non-fuzzy measures". Fuzzy Sets and Systems 158, nr 11 (czerwiec 2007): 1258–72. http://dx.doi.org/10.1016/j.fss.2006.12.011.

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31

Bhutani, Kiran R., i Azriel Rosenfeld. "Dissimilarity measures between fuzzy sets or fuzzy structures". Information Sciences 152 (czerwiec 2003): 313–18. http://dx.doi.org/10.1016/s0020-0255(03)00076-8.

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32

LIU, SHIANG-TAI. "FUZZY MEASURES FOR FUZZY SIGNAL-TO-NOISE RATIOS". Cybernetics and Systems 36, nr 2 (18.01.2005): 151–63. http://dx.doi.org/10.1080/01969720590897161.

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33

Dumitrescu, D. "Fuzzy Measures and the Entropy of Fuzzy Partitions". Journal of Mathematical Analysis and Applications 176, nr 2 (lipiec 1993): 359–73. http://dx.doi.org/10.1006/jmaa.1993.1220.

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34

Gupta, Priti, i Santosh Kumari. "On Bounds For Weighted Fuzzy Mean Difference-Divergence Measures". International Journal of Scientific Research 3, nr 6 (1.06.2012): 239–49. http://dx.doi.org/10.15373/22778179/june2014/78.

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35

Xue, Yige, i Yong Deng. "Extending Set Measures to Orthopair Fuzzy Sets". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, nr 01 (luty 2022): 63–91. http://dx.doi.org/10.1142/s0218488522500040.

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Yager have proposed the extending set measures to Pythagorean fuzzy sets, which is able to efficiently solve the problems of uncertain information representation in Pythagorean fuzzy environment. However, the Pythagorean fuzzy sets represent a limited range of fields, while the q-rung orthopair fuzzy sets can represent many fuzzy sets, including the Pythagorean fuzzy sets, Fermatean fuzzy sets, and intuitionistic fuzzy sets. In order to extend the extending set measures to Pythagorean fuzzy sets to a broader range, the paper proposes the extending set measures to orthopair fuzzy sets, which can extend the extending set measures to Pythagorean fuzzy sets to q-rung orthopair fuzzy environment. The extending set measures to orthopair fuzzy sets combined with level sets, measure, principle extension and Choquet Integral, which can greatly extend the ability of representing unknown information and processing unknown information. If the q-rung orthopair fuzzy sets degenerate into to Pythagorean fuzzy sets, then the extending set measures to orthopair fuzzy sets will be generated as the extending set measures to Pythagorean fuzzy sets. Numerical examples are designed to prove the effectiveness of the proposed models and the experimental results demonstrate that the proposed method can extend the extending set measures to Pythagorean fuzzy sets to q-rung orthopair fuzzy environment successfully and solve issues of decision making under q-rung orthopair fuzzy environment effectively.

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36

MARICHAL, JEAN-LUC, i MARC ROUBENS. "ENTROPY OF DISCRETE FUZZY MEASURES". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, nr 06 (grudzień 2000): 625–40. http://dx.doi.org/10.1142/s0218488500000460.

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The concept of entropy of a discrete fuzzy measure has been recently introduced in two different ways. A first definition was proposed by Marichal in the aggregation framework, and a second one by Yager in the framework of uncertain variables. We present a comparative study between these two proposals and point out their properties. We also propose a definition for the entropy of an ordinal fuzzy measure, that is, a fuzzy measure taking its values in an ordinal scale in the sense of measurement theory.

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37

Kobza, Vladimír. "Divergence measures on intuitionistic fuzzy sets". Notes on Intuitionistic Fuzzy Sets 28, nr 4 (12.12.2022): 413–27. http://dx.doi.org/10.7546/nifs.2022.28.4.413-427.

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The basic study of fuzzy sets theory was introduced by Lotfi Zadeh in 1965. Many authors investigated possibilities how two fuzzy sets can be compared and the most common kind of measures used in the mathematical literature are dissimilarity measures. The previous approach to the dissimilarities is too restrictive, because the third axiom in the definition of dissimilarity measure assumes the inclusion relation between fuzzy sets. While there exist many pairs of fuzzy sets, which are incomparable to each other with respect to the inclusion relation. Therefore we need some new concept for measuring a difference between fuzzy sets so that it could be applied for arbitrary fuzzy sets. We focus on the special class of so called local divergences. In the next part we discuss the divergences defined on more general objects, namely intuitionistic fuzzy sets. In this case we define the local property modified to this object. We discuss also the relation of usual divergences between fuzzy sets to the divergences between intuitionistic fuzzy sets.

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38

MIRANDA, PEDRO, i MICHEL GRABISCH. "OPTIMIZATION ISSUES FOR FUZZY MEASURES". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, nr 06 (grudzień 1999): 545–60. http://dx.doi.org/10.1142/s0218488599000477.

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In this paper, we address the problem of identification of fuzzy measures through different representations, namely the Möbius, the Shapley and the Banzhaf interaction representations. In the first part of the paper, we recall the main results concerning these representations, and give a simple algorithm to compute them. Then we determine the bounds of the Möbius and the interaction representations for fuzzy measures. Lastly, the identification of fuzzy measures by minimizing a quadratic error criterion is addressed. We give expressions of the quadratic program for all the considered representations, and study the uniqueness of the solution.

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39

MESIAR, RADKO. "k–ORDER ADDITIVE FUZZY MEASURES". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, nr 06 (grudzień 1999): 561–68. http://dx.doi.org/10.1142/s0218488599000489.

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A generalization of k-order additive discrete fuzzy measures recently introduced by Grabisch is shown. k-order additive fuzzy measures on general spaces are introduced. Connection of the proposed generalization with the general Möbius transform of Shafer is shown. General evaluation formula for the Choquet integral is given. Further generalizations concerning the type of applied arithmetics are proposed.

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40

Çetkin, Vildan. "On measures of parameterized fuzzy compactness". Filomat 34, nr 9 (2020): 2927–38. http://dx.doi.org/10.2298/fil2009927c.

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In the present study, the parameterized degree of compactness of a lattice valued fuzzy soft set is described in a fuzzy soft topological space. The extended versions of the basic compactness properties known in general topology are investigated for the given notion and some further characterizations of parameterized degree of fuzzy compactness are specified. In addition, a generalized version of Tychonoff Theorem is proved in the product fuzzy soft topological space.

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41

DING, Shi-Fei, Hong ZHU, Xin-Zheng XU i Zhong-Zhi SHI. "Entropy-Based Fuzzy Information Measures". Chinese Journal of Computers 35, nr 4 (16.11.2012): 796–801. http://dx.doi.org/10.3724/sp.j.1016.2012.00796.

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42

FUJIMOTO, Katsushige. "Interaction indices for fuzzy measures." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 16, nr 4 (2004): 303–10. http://dx.doi.org/10.3156/jsoft.16.303.

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43

R. Yager, Ronald. "Uncertainty modeling using fuzzy measures". Knowledge-Based Systems 92 (styczeń 2016): 1–8. http://dx.doi.org/10.1016/j.knosys.2015.10.001.

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44

Mesiar, R., A. Mesiarová-Zemánková i L’ Valášková. "Basic generated universal fuzzy measures". International Journal of Approximate Reasoning 46, nr 3 (grudzień 2007): 447–57. http://dx.doi.org/10.1016/j.ijar.2006.12.012.

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45

Delgado, M., i S. Moral. "Upper and lower fuzzy measures". Fuzzy Sets and Systems 33, nr 2 (listopad 1989): 191–200. http://dx.doi.org/10.1016/0165-0114(89)90240-6.

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46

Sgarro, Andrea. "Fuzziness measures for fuzzy rectangles". Fuzzy Sets and Systems 34, nr 1 (styczeń 1990): 39–45. http://dx.doi.org/10.1016/0165-0114(90)90125-p.

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47

Jiang, Qingshan, i Hisakichi Suzuki. "Fuzzy measures on metric spaces". Fuzzy Sets and Systems 83, nr 1 (październik 1996): 99–106. http://dx.doi.org/10.1016/0165-0114(95)00304-5.

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48

Guo, Caimei, i Deli Zhang. "On set-valued fuzzy measures". Information Sciences 160, nr 1-4 (marzec 2004): 13–25. http://dx.doi.org/10.1016/j.ins.2003.07.006.

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49

Yager, Ronald R. "Probability measures over fuzzy spaces". International Journal of General Systems 36, nr 3 (czerwiec 2007): 251–62. http://dx.doi.org/10.1080/03081070601058604.

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50

Yager, R. R. "Uncertainty representation using fuzzy measures". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, nr 1 (2002): 13–20. http://dx.doi.org/10.1109/3477.979955.

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