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Journal articles on the topic 'Fuzzy'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 October 2024

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1

Şengönül, M., and Z. Zararsız. "Some Additions to the Fuzzy Convergent and Fuzzy Bounded Sequence Spaces of Fuzzy Numbers." Abstract and Applied Analysis 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/837584.

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Some properties of the fuzzy convergence and fuzzy boundedness of a sequence of fuzzy numbers were studied in Choi (1996). In this paper, we have consider, some important problems on these spaces and shown that these spaces are fuzzy complete module spaces. Also, the fuzzyα-, fuzzyβ-, and fuzzyγ-duals of the fuzzy module spaces of fuzzy numbers have been computeded, and some matrix transformations are given.

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2

Zhang, Xiaohong. "On Some Fuzzy Filters in Pseudo-BCIAlgebras." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/718972.

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Some new properties of fuzzy associative filters (also known as fuzzy associative pseudo-filters), fuzzyp-filter (also known as fuzzy pseudo-p-filters), and fuzzya-filter (also known as fuzzy pseudo-a-filters) in pseudo-BCIalgebras are investigated. By these properties, the following important results are proved: (1) a fuzzy filter (also known as fuzzy pseudo-filters) of a pseudo-BCIalgebra is a fuzzy associative filter if and only if it is a fuzzya-filter; (2) a filter (also known as pseudo-filter) of a pseudo-BCIalgebra is associative if and only if it is ana-filter (also call it pseudo-afilter); (3) a fuzzy filter of a pseudo-BCIalgebra is fuzzya-filter if and only if it is both a fuzzyp-filter and a fuzzyq-filter.

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3

Mukherjee, M. N., and S. P. Sinha. "Fuzzyθ-closure operator on fuzzy topological spaces." International Journal of Mathematics and Mathematical Sciences 14, no. 2 (1991): 309–14. http://dx.doi.org/10.1155/s0161171291000364.

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The paper contains a study of fuzzyθ-closure operator,θ-closures of fuzzy sets in a fuzzy topological space are characterized and some of their properties along with their relation with fuzzyδ-closures are investigated. As applications of these concepts, certain functions as well as some spaces satisfying certain fuzzy separation axioms are characterized in terms of fuzzyθ-closures andδ-closures.

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4

Kumbhojkar, H. V. "Proper Fuzzification of Prime Ideals of a Hemiring." Advances in Fuzzy Systems 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/801650.

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Prime fuzzy ideals, prime fuzzyk-ideals, and prime fuzzyh-ideals are roped in one condition. It is shown that this way better fuzzification is achieved. Other major results of the paper are: every fuzzy ideal (resp.,k-ideal,h-ideal) is contained in a prime fuzzy ideal (resp.,k-ideal,h-ideal). Prime radicals and nil radicals of a fuzzy ideal are defined; their relationship is established. The nil radical of a fuzzyk-ideal (resp., anh-ideal) is proved to be a fuzzyk-ideal (resp.,h-ideal). The correspondence theorems for different types of fuzzy ideals of hemirings are established. The concept of primary fuzzy ideal is introduced. Minimum imperative for proper fuzzification is suggested and it is shown that the fuzzifications introduced in this paper are proper fuzzifications.

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Uehara, Kiyohiko, and Kaoru Hirota. "A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 2 (March 20, 2021): 213–25. http://dx.doi.org/10.20965/jaciii.2021.p0213.

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A method is proposed for evaluating fuzzy rules independently of each other in fuzzy rules learning. The proposed method is named α-FUZZI-ES (α-weight-based fuzzy-rule independent evaluations) in this paper. In α-FUZZI-ES, the evaluation value of a fuzzy system is divided out among the fuzzy rules by using the compatibility degrees of the learning data. By the effective use of α-FUZZI-ES, a method for fast fuzzy rules learning is proposed. This is named α-FUZZI-ES learning (α-FUZZI-ES-based fuzzy rules learning) in this paper. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rules learning. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rules optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. Numerical results show that α-FUZZI-ES learning is superior to the exemplary conventional scheme in terms of accuracy and convergence speed when the evaluation function is non-differentiable.

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6

Lee, Seok Jong, and Eun Pyo Lee. "Fuzzyr-continuous and fuzzyr-semicontinuous maps." International Journal of Mathematics and Mathematical Sciences 27, no. 1 (2001): 53–63. http://dx.doi.org/10.1155/s0161171201010882.

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We introduce a new notion of fuzzyr-interior which is an extension of Chang's fuzzy interior. Using fuzzyr-interior, we define fuzzyr-semiopen sets and fuzzyr-semicontinuous maps which are generalizations of fuzzy semiopen sets and fuzzy semicontinuous maps in Chang's fuzzy topology, respectively. Some basic properties of fuzzyr-semiopen sets and fuzzyr-semicontinuous maps are investigated.

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7

Jeon, Joung Kon, Young Bae Jun, and Jin Han Park. "Intuitionistic fuzzy alpha-continuity and intuitionistic fuzzy precontinuity." International Journal of Mathematics and Mathematical Sciences 2005, no. 19 (2005): 3091–101. http://dx.doi.org/10.1155/ijmms.2005.3091.

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A characterization of intuitionistic fuzzyα-open set is given, and conditions for an IFS to be an intuitionistic fuzzyα-open set are provided. Characterizations of intuitionistic fuzzy precontinuous (resp.,α-continuous) mappings are given.

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8

Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

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Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzyC-means clustering, fuzzyK-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.

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9

Ma, Zhen Ming. "Some Types of Generalized Fuzzyn-Fold Filters in Residuated Lattices." Abstract and Applied Analysis 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/736872.

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Fuzzy filters and their generalized types have been extensively studied in the literature. In this paper, a one-to-one correspondence between the set of all generalized fuzzy filters and the set of all generalized fuzzy congruences is established, a quotient residuated lattice with respect to generalized fuzzy filter is induced, and several types of generalized fuzzyn-fold filters such as generalized fuzzyn-fold positive implicative (fantastic and Boolean) filters are introduced; examples and results are provided to demonstrate the relations among these filters.

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10

Renuka, R., and V. Seenivasan. "On Intuitionistic Fuzzyβ-Almost Compactness andβ-Nearly Compactness." Scientific World Journal 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/869740.

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The concept of intuitionistic fuzzyβ-almost compactness and intuitionistic fuzzyβ-nearly compactness in intuitionistic fuzzy topological spaces is introduced and studied. Besides giving characterizations of these spaces, we study some of their properties. Also, we investigate the behavior of intuitionistic fuzzyβ-compactness, intuitionistic fuzzyβ-almost compactness, and intuitionistic fuzzyβ-nearly compactness under several types of intuitionistic fuzzy continuous mappings.

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11

Shi, Fu-Gui. "(L,M)-Fuzzyσ-Algebras." International Journal of Mathematics and Mathematical Sciences 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/356581.

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The notion of (L,M)-fuzzyσ-algebras is introduced in the lattice value fuzzy set theory. It is a generalization of Klement's fuzzyσ-algebras. In our definition of (L,M)-fuzzyσ-algebras, eachL-fuzzy subset can be regarded as anL-measurable set to some degree.

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12

Abbas, S. E. "Fuzzy super irresolute functions." International Journal of Mathematics and Mathematical Sciences 2003, no. 42 (2003): 2689–700. http://dx.doi.org/10.1155/s0161171203212278.

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The concept of fuzzy super irresolute function was considered and studied by ostak's (1985). A comparison between this type and other existing ones is established. Several characterizations, properties, and their effect on some fuzzy topological spaces are studied. Also, a new class of fuzzy topological spaces under the terminology fuzzyS∗-closed spaces is introduced and investigated.

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13

Cong, Cuong Bui, Roan Thi Ngan, and Le Ba Long. "Some New De Morgan Picture Operator Triples in Picture Fuzzy Logic." Journal of Computer Science and Cybernetics 33, no. 2 (January 3, 2018): 143–64. http://dx.doi.org/10.15625/1813-9663/33/2/10706.

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A new concept of picture fuzzy sets (PFS) were introduced in 2013, which are directextensions of the fuzzy sets and the intuitonistic fuzzy sets. Then some operations on PFS withsome properties are considered in [ 9,10 ]. Some basic operators of fuzzy logic as negation, tnorms, t-conorms for picture fuzzy sets firstly are defined and studied in [13,14]. This paper isdevoted to some classes of representable picture fuzzy t-norms and representable picture fuzzyt-conorms on PFS and a basic algebra structure of Picture Fuzzy Logic – De Morgan triples ofpicture operators.

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14

Jianming, Zhan, and Tan Zhisong. "Doubt fuzzy BCI-algebras." International Journal of Mathematics and Mathematical Sciences 30, no. 1 (2002): 49–56. http://dx.doi.org/10.1155/s0161171202007986.

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The aim of this note is to introduce the notion of doubt fuzzyp-ideals in BCI-algebras and to study their properties. We also solve the problem of classifying doubt fuzzyp-ideals and study fuzzy relations on BCI-algebras.

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15

Thakur, S. S., and Annamma Philip. "Pairwise fuzzy connectedness between fuzzy sets." Mathematica Bohemica 122, no. 4 (1997): 375–80. http://dx.doi.org/10.21136/mb.1997.126217.

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16

Taleshian, Fatemeh, and Jafar Fathali. "A Mathematical Model for Fuzzyp-Median Problem with Fuzzy Weights and Variables." Advances in Operations Research 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7590492.

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We investigate thep-median problem with fuzzy variables and weights of vertices. The fuzzy equalities and inequalities transform to crisp cases by using some technique used in fuzzy linear programming. We show that the fuzzy objective function also can be replaced by crisp functions. Therefore an auxiliary linear programming model is obtained for the fuzzyp-median problem. The results are compared with two previously proposed methods.

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17

Kanzawa, Yuchi. "Fuzzy Co-Clustering Algorithms Based on Fuzzy Relational Clustering and TIBA Imputation." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 2 (March 20, 2014): 182–89. http://dx.doi.org/10.20965/jaciii.2014.p0182.

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In this paper, two types of fuzzy co-clustering algorithms are proposed. First, it is shown that the base of the objective function for the conventional fuzzy co-clustering method is very similar to the base for entropy-regularized fuzzy nonmetric model. Next, it is shown that the non-sense clustering problem in the conventional fuzzy co-clustering algorithms is identical to that in fuzzy nonmetric model algorithms, in the case that all dissimilarities among rows and columns are zero. Based on this discussion, a method is proposed applying entropy-regularized fuzzy nonmetric model after all dissimilarities among rows and columns are set to some values using a TIBA imputation technique. Furthermore, since relational fuzzy cmeans is similar to fuzzy nonmetricmodel, in the sense that both methods are designed for homogeneous relational data, a method is proposed applying entropyregularized relational fuzzyc-means after imputing all dissimilarities among rows and columns with TIBA. Some numerical examples are presented for the proposed methods.

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18

Aly, S., and I. Vrana. "Multiple parallel fuzzy expert systems utilizing a hierarchical fuzz model." Agricultural Economics (Zemědělská ekonomika) 53, No. 2 (January 7, 2008): 89–93. http://dx.doi.org/10.17221/1425-agricecon.

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Business, economic, and agricultural YES-or-NO decision making problems often require multiple, different and specific expertises. This is due to the nature of such problems in which decisions may be influenced by multiple different, relevant aspects, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertises due to its capability to model real world values, which are not always exact, but frequently vague or uncertain. In this research, different expertises, relevant to the decision solution, are modeled using several corresponding FESs. Every FES produces a crisp numerical output expressing the degree of bias toward “Yes” or “No“ decision. A unified scale is standardized for numerical outputs of all FESs. This scale ranges from 0 to 10, where the value 0 represents a complete bias ”No“ decision and the value 10 represents a complete bias to ”Yes“ decision. Intermediate values reflect the degree of bias either to ”Yes“ or ”No“ decision. These systems are then integrated to comprehensibly judge the binary decision problem, which requires all such expertises. Practically, the main reasons for independency among the multiple FESs can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. The proposed mechanism for realizing integration is a hierarchical fuzzy system (HFS) based model, which allows the utilization of the existing If-then knowledge about how to combine/aggregate the outputs of FESs.

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19

Hussain, MA, and YY Yousif. "Fibrewise Fuzzy Separation Axioms." Journal of Physics: Conference Series 2322, no. 1 (August 1, 2022): 012062. http://dx.doi.org/10.1088/1742-6596/2322/1/012062.

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Abstract Within that research, we introduce fibrewise fuzzy types of the most important separation axioms in ordinary fuzz topology, namely fibrewise fuzzy (T 0 spaces, T 1 spaces, R 0 spaces, Hausdorff spaces, functionally Hausdorff spaces, regular spaces, completely regular spaces, normal spaces, and normal spaces). Too we add numerous outcomes about it.

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20

Yazdi, Hadi Sadoghi, Mehri Sadoghi Yazdi, and Abedin Vahedian. "Fuzzy Bayesian Classification of LR Fuzzy Numbers." International Journal of Engineering and Technology 1, no. 5 (2009): 415–23. http://dx.doi.org/10.7763/ijet.2009.v1.78.

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21

Ramadan, A. A., M. El-Dardery, and Hu Zhao. "On Convergence inL-Valued Fuzzy Topological Spaces." Abstract and Applied Analysis 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/730940.

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We introduce the concept ofL-fuzzy neighborhood systems using completeMV-algebras and present important links with the theory ofL-fuzzy topological spaces. We investigate the relationships among the degrees ofL-fuzzyr-adherent points (r-convergent,r-cluster, andr-limit, resp.) in anL-fuzzy topological spaces. Also, we investigate the concept ofLF-continuous functions and their properties.

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22

Kanaujia, Alka, Parijat Sinha, and Manjari Srivastava. "FUZZY SEMI-SEPARATION AXIOMS AND FUZZY SEMI-CONNECTEDNESS IN FUZYY BICLOSURE SPACES." jnanabha 53, no. 01 (2023): 62–67. http://dx.doi.org/10.58250/jnanabha.2023.53107.

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The purpose of this paper is to introduce the notion of fuzzy semi-separation axioms and fuzzy semi-connectedness in fuzzy biclosure spaces. Further we investigate their characterizations and find relations with other already existing definitions. We generalize the results of semi-connectedness in fuzzy setting. Here we follow the definition of closure operator given by Birkhoff [5].

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23

Akram, M., and K. H. Dar. "T-fuzzy ideals inBCI-algebras." International Journal of Mathematics and Mathematical Sciences 2005, no. 12 (2005): 1899–907. http://dx.doi.org/10.1155/ijmms.2005.1899.

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24

Cat, Jordi. "Fuzzy Empiricism and Fuzzy‐Set Causality: What Is All the Fuzz About?*." Philosophy of Science 73, no. 1 (January 2006): 26–41. http://dx.doi.org/10.1086/510173.

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25

Chattopadhyay, K. C., and S. K. Samanta. "Fuzzy topology: Fuzzy closure operator, fuzzy compactness and fuzzy connectedness." Fuzzy Sets and Systems 54, no. 2 (March 1993): 207–12. http://dx.doi.org/10.1016/0165-0114(93)90277-o.

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26

Wu, Congxin, Deli Zhang, Bokan Zhang, and 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, no. 1 (January 1999): 137–41. http://dx.doi.org/10.1016/s0165-0114(97)00041-9.

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27

R, Shanmugapriya, and Mary Jiny D. "Fuzzy Super Resolving Number of Fuzzy Labelling Graphs." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 606–11. http://dx.doi.org/10.5373/jardcs/v11/20192612.

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28

Mohite, Trupti. "On Convexity of Fuzzy Sets and Fuzzy Relations." International Journal of Science and Research (IJSR) 12, no. 7 (July 5, 2023): 758–61. http://dx.doi.org/10.21275/sr23710111006.

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29

Roh, Eun Hwan, Young Bae Jun, and Wook Hwan Shim. "Fuzzy associativeℐ-ideals ofIS-algebras." International Journal of Mathematics and Mathematical Sciences 24, no. 11 (2000): 729–35. http://dx.doi.org/10.1155/s0161171200004105.

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30

Khan, Muhammad Bilal, Pshtiwan Othman Mohammed, Muhammad Aslam Noor, Abdullah M. Alsharif, and Khalida Inayat Noor. "New fuzzy-interval inequalities in fuzzy-interval fractional calculus by means of fuzzy order relation." AIMS Mathematics 6, no. 10 (2021): 10964–88. http://dx.doi.org/10.3934/math.2021637.

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<abstract> <p>It is well-known that interval analysis provides tools to deal with data uncertainty. In general, interval analysis is typically used to deal with the models whose data are composed of inaccuracies that may occur from certain kinds of measurements. In interval analysis and fuzzy-interval analysis, the inclusion relation (⊆) and fuzzy order relation $\left(\preccurlyeq \right)$ both are two different concepts, respectively. In this article, with the help of fuzzy order relation, we introduce fractional Hermite-Hadamard inequality (<italic>HH</italic>-inequality) for <italic>h</italic>-convex fuzzy-interval-valued functions (<italic>h</italic>-convex-IVFs). Moreover, we also establish a strong relationship between <italic>h</italic>-convex fuzzy-IVFs and Hermite-Hadamard Fejér inequality (<italic>HH</italic>-Fejér inequality) via fuzzy Riemann Liouville fractional integral operator. It is also shown that our results include a wide class of new and known inequalities for <italic>h</italic>-convex fuzz-IVFs and their variant forms as special cases. Nontrivial examples are presented to illustrate the validity of the concept suggested in this review. This paper's techniques and approaches may serve as a springboard for further research in this field.</p> </abstract>

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31

Wang, Zhenyuan, and Li Zhang-Westmant. "New Ranking Method For Fuzzy Numbers By Their Expansion Center." Journal of Artificial Intelligence and Soft Computing Research 4, no. 3 (July 1, 2014): 181–87. http://dx.doi.org/10.1515/jaiscr-2015-0007.

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Abstract Based on the area between the curve of the membership function of a fuzzy number and the horizontal real axis, a characteristic as a new numerical index, called the expansion center, for fuzzy numbers is proposed. An intuitive and reasonable ranking method for fuzzy numbers based on this characteristic is also established. The new ranking method is applicable for decision making and data analysis in fuzz environments. An important criterion of the goodness for ranking fuzzy numbers, the geometric intuitivity, is also introduced. It guarantees coinciding with the natural ordering of the real numbers.

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32

Hong, Liang. "Fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections." Annals of West University of Timisoara - Mathematics and Computer Science 53, no. 1 (July 1, 2015): 77–108. http://dx.doi.org/10.1515/awutm-2015-0005.

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Abstract Fuzzy ordered linear spaces, Riesz spaces, fuzzy Archimedean spaces and σ-complete fuzzy Riesz spaces were defined and studied in several works. Following the efforts along this line, we define fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections and establish their fundamental properties.

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33

Mahmoud, F. S., M. A. Fath Alla, and S. M. Abd Ellah. "Fuzzy topology on fuzzy sets: fuzzy semicontinuity and fuzzy semiseparation axioms." Applied Mathematics and Computation 153, no. 1 (May 2004): 127–40. http://dx.doi.org/10.1016/s0096-3003(03)00616-7.

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34

Ovchinnikov, Sergei. "Fuzzy algebra. Volume 1. Fuzzy subgroups, fuzzy subrings and fuzzy ideals." Fuzzy Sets and Systems 65, no. 2-3 (August 1994): 331. http://dx.doi.org/10.1016/0165-0114(94)90038-8.

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35

Gammoudi, Aymen, Allel Hadjali, and Boutheina Ben Yaghlane. "Fuzz-TIME: an intelligent system for managing fuzzy temporal information." International Journal of Intelligent Computing and Cybernetics 10, no. 2 (June 12, 2017): 200–222. http://dx.doi.org/10.1108/ijicc-09-2016-0036.

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Purpose Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is subjective and fuzzy. The purpose of this paper is to address the issue related to the modeling and handling of imperfection inherent to both temporal relations and intervals. Design/methodology/approach On the one hand, fuzzy extensions of Allen temporal relations are investigated and, on the other hand, extended temporal relations to define the positions of two fuzzy time intervals are introduced. Then, a database system, called Fuzzy Temporal Information Management and Exploitation (Fuzz-TIME), is developed for the purpose of processing fuzzy temporal queries. Findings To evaluate the proposal, the authors have implemented a Fuzz-TIME system and created a fuzzy historical database for the querying purpose. Some demonstrative scenarios from history domain are proposed and discussed. Research limitations/implications The authors have conducted some experiments on archaeological data to show the effectiveness of the Fuzz-TIME system. However, thorough experiments on large-scale databases are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria. Practical implications The tool developed (Fuzz-TIME) can have many practical applications where time information has to be dealt with. In particular, in several real-world applications like history, medicine, criminal and financial domains, where time is often perceived or expressed in an imprecise/fuzzy manner. Social implications The social implications of this work can be expected, more particularly, in two domains: in the museum to manage, exploit and analysis the piece of information related to archives and historic data; and in the hospitals/medical organizations to deal with time information inherent to data about patients and diseases. Originality/value This paper presents the design and characterization of a novel and intelligent database system to process and manage the imperfection inherent to both temporal relations and intervals.

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Honda, Katsuhiro, Shunnya Oshio, and Akira Notsu. "Fuzzy Co-Clustering Induced by Multinomial Mixture Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 717–26. http://dx.doi.org/10.20965/jaciii.2015.p0717.

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A close connection between fuzzyc-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms were induced by the GMMs concept, where fuzzy partitions are proved to be more useful for revealing intrinsic cluster structures than probabilistic ones. Co-clustering is a promising technique for summarizing cooccurrence information such as document-keyword frequencies. In this paper, a fuzzy co-clustering model is induced based on the multinomial mixture models (MMMs) concept, in which the degree of fuzziness of both object and item fuzzy memberships can be properly tuned. The advantages of the dual fuzzy partition are demonstrated through several experimental results including document clustering applications.

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Guang-Quan, Zhang. "Fuzzy number-valued fuzzy measure and fuzzy number-valued fuzzy integral on the fuzzy set." Fuzzy Sets and Systems 49, no. 3 (August 1992): 357–76. http://dx.doi.org/10.1016/0165-0114(92)90287-e.

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38

Tebu, S. F., C. Lele, and J. B. Nganou. "Fuzzy -Fold BCI-Positive Implicative Ideals in BCI-Algebras." International Journal of Mathematics and Mathematical Sciences 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/628931.

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We consider the notion of fuzzy -fold positive implicative ideals in BCI-algebras. We analyse many properties of fuzzy -fold positive implicative ideals. We also establish an extension properties for fuzzyn-fold positive implicative ideals in BCI-algberas. This work generalizes the corresponding results in the crisp case.

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39

Congxin Wu, Deli Zhang, Caimei Guo, and 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, no. 3 (September 1998): 355–60. http://dx.doi.org/10.1016/s0165-0114(96)00394-6.

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40

Qiu, Wangren, Ping Zhang, and Yanhong Wang. "Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/962597.

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In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS(M, N)based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parametersMandN, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.

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Chetty, G. Palani, and G. Balasubramanian. "On fuzzy nearly C-compactness in fuzzy topological spaces." Mathematica Bohemica 132, no. 1 (2007): 1–12. http://dx.doi.org/10.21136/mb.2007.133991.

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Spinneli, J. Michael Anna. "Properties of Anti- Fuzzy Kernel and Anti- Fuzzy Subsemiautomata." International Journal of Scientific Research 2, no. 12 (June 1, 2012): 348–49. http://dx.doi.org/10.15373/22778179/dec2013/107.

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43

Kungumaraj, E., and K. Nirmaladevi. "Fuzzy Strongly gs Closed sets in Fuzzy Topological spaces." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 857–59. http://dx.doi.org/10.31142/ijtsrd9482.

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Liu, Qi, and Jianming Zhan. "IFP-Intuitionistic Fuzzy Softh-Ideals of Hemirings and Its Decision Making." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/589465.

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Abstract:

The aim of this paper is to introduce the concepts of IFP-intuitionistic fuzzy softh-ideals and IFP-equivalent intuitionistic fuzzy softh-ideals of hemirings. Some characterizations and properties of them are given. In particular, some good examples are explored. Finally, we investigate aggregate intuitionistic fuzzyh-ideals of hemirings based on decision making.

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Sikdar, Subhas. "Fuzzy clean, fuzzy green." Clean Technologies and Environmental Policy 19, no. 6 (June 24, 2017): 1585–87. http://dx.doi.org/10.1007/s10098-017-1378-1.

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Demirci, M., and J. Recasens. "Fuzzy groups, fuzzy functions and fuzzy equivalence relations." Fuzzy Sets and Systems 144, no. 3 (June 2004): 441–58. http://dx.doi.org/10.1016/s0165-0114(03)00301-4.

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Caimei Guo, Deli Zhang, and Congxin Wu. "Fuzzy-valued fuzzy measures and generalized fuzzy integrals." Fuzzy Sets and Systems 97, no. 2 (July 1998): 255–60. http://dx.doi.org/10.1016/s0165-0114(96)00276-x.

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Tiwari, S. P., and Arun K. Srivastava. "Fuzzy rough sets, fuzzy preorders and fuzzy topologies." Fuzzy Sets and Systems 210 (January 2013): 63–68. http://dx.doi.org/10.1016/j.fss.2012.06.001.

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Dib, K. A., and Nabil L. Youssef. "Fuzzy Cartesian product, fuzzy relations and fuzzy functions." Fuzzy Sets and Systems 41, no. 3 (June 1991): 299–315. http://dx.doi.org/10.1016/0165-0114(91)90134-c.

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Hanafy, I. M., F. S. Mahmoud, and M. M. Khalaf. "Fuzzy Topology On Fuzzy Sets: Fuzzy γ-Continuity and Fuzzy γ-Retracts." International Journal of Fuzzy Logic and Intelligent Systems 5, no. 1 (March 1, 2005): 29–34. http://dx.doi.org/10.5391/ijfis.2005.5.1.029.

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