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Journal articles on the topic 'Fuzzy Methods'

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Author: Grafiati

Published: 10 March 2023

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1

TAKAHAGI, Eiichiro. "Fuzzy Integrals and Fuzzy Reasoning Methods." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 1 (2015): 12–19. http://dx.doi.org/10.3156/jsoft.27.1_12.

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2

Subbotin, Igor Ya, and Michael Gr Voskoglou. "Fuzzy Assessment Methods." Universal Journal of Applied Mathematics 2, no. 9 (December 2014): 305–11. http://dx.doi.org/10.13189/ujam.2014.020902.

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3

MIZUMOTO, Masaharu. "Fuzzy controls methods." Journal of the Robotics Society of Japan 6, no. 6 (1988): 528–35. http://dx.doi.org/10.7210/jrsj.6.6_528.

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4

Perrone, G., and S. Noto La Diega. "Fuzzy methods for analysing fuzzy production environment." Robotics and Computer-Integrated Manufacturing 14, no. 5-6 (October 1998): 465–74. http://dx.doi.org/10.1016/s0736-5845(98)00021-0.

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5

Mizumoto, Masaharu. "Fuzzy controls under various fuzzy reasoning methods." Information Sciences 45, no. 2 (July 1988): 129–51. http://dx.doi.org/10.1016/0020-0255(88)90037-0.

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6

Kumar, Amit, and Amarpreet Kaur. "Methods for Solving Fully Fuzzy Transportation Problems Based on Classical Transportation Methods." International Journal of Operations Research and Information Systems 2, no. 4 (October 2011): 52–71. http://dx.doi.org/10.4018/joris.2011100104.

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There are several methods, in literature, for finding the fuzzy optimal solution of fully fuzzy transportation problems (transportation problems in which all the parameters are represented by fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings, two new methods (based on fuzzy linear programming formulation and classical transportation methods) are proposed to find the fuzzy optimal solution of unbalanced fuzzy transportation problems by representing all the parameters as trapezoidal fuzzy numbers. The advantages of the proposed methods over existing methods are also discussed. To illustrate the proposed methods a fuzzy transportation problem (FTP) is solved by using the proposed methods and the obtained results are discussed. The proposed methods are easy to understand and to apply for finding the fuzzy optimal solution of fuzzy transportation problems occurring in real life situations.

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7

WATADA, Junzo. "Methods for Fuzzy Classification." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 61–73. http://dx.doi.org/10.3156/jfuzzy.4.1_61.

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8

FURUTA, Hitoshi. "Fuzzy Methods and Design." Journal of the Society of Mechanical Engineers 99, no. 928 (1996): 185–87. http://dx.doi.org/10.1299/jsmemag.99.928_185.

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9

Işik, Can, and Salwa Ammar. "Fuzzy optimal search methods." Fuzzy Sets and Systems 46, no. 3 (March 1992): 331–37. http://dx.doi.org/10.1016/0165-0114(92)90371-a.

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10

Hu, Yi-Chung, Ruey-Shun Chen, and Gwo-Hshiung Tzeng. "Discovering fuzzy association rules using fuzzy partition methods." Knowledge-Based Systems 16, no. 3 (April 2003): 137–47. http://dx.doi.org/10.1016/s0950-7051(02)00079-5.

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11

Lodwick, Weldon A., and K. David Jamison. "Interval Methods and Fuzzy Optimization." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 03 (June 1997): 239–49. http://dx.doi.org/10.1142/s0218488597000221.

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In this paper, we describe interval-based methods for solving constrained fuzzy optimization problems. The class of fuzzy functions we consider for the optimization problems is the set of real-valued functions where one or more parameters/coefficients are fuzzy numbers. The focus of this research is to explore some relationships between fuzzy set theory and interval analysis as it relates to optimization problems.

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12

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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13

LI, DENGFENG, and CHUNTIAN CHENG. "FUZZY MULTIOBJECTIVE PROGRAMMING METHODS FOR FUZZY CONSTRAINED MATRIX GAMES WITH FUZZY NUMBERS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 04 (August 2002): 385–400. http://dx.doi.org/10.1142/s0218488502001545.

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The purpose of the paper is to introduce a new type of fuzzy matrix games: fuzzy constrained matrix games. A computational method for its solution based on establishment of the auxiliary fuzzy linear programming for each player is proposed. The approach based on the multiobjective programming is establisched to solve these fuzzy linear programming. Effectiveness is illustrated with a numerical example.

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14

ABDALLA, AREEG, and JAMES BUCKLEY. "MONTE CARLO METHODS IN FUZZY GAME THEORY." New Mathematics and Natural Computation 03, no. 02 (July 2007): 259–69. http://dx.doi.org/10.1142/s1793005707000768.

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In this paper, we consider a two-person zero-sum game with fuzzy payoffs and fuzzy mixed strategies for both players. We define the fuzzy value of the game for both players [Formula: see text] and also define an optimal fuzzy mixed strategy for both players. We then employ our fuzzy Monte Carlo method to produce approximate solutions, to an example fuzzy game, for the fuzzy values [Formula: see text] for Player I and [Formula: see text] for Player II; and also approximate solutions for the optimal fuzzy mixed strategies for both players. We then look at [Formula: see text] and [Formula: see text] to see if there is a Minimax theorem [Formula: see text] for this fuzzy game.

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15

Simhachalam, B., and G. Ganesan. "Performance comparison of fuzzy and non-fuzzy classification methods." Egyptian Informatics Journal 17, no. 2 (July 2016): 183–88. http://dx.doi.org/10.1016/j.eij.2015.10.004.

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16

Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data." Advances in Fuzzy Systems 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/265170.

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Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.

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17

INNOCENT, P. R., R. I. JOHN, and J. M. GARIBALDI. "FUZZY METHODS FOR MEDICAL DIAGNOSIS." Applied Artificial Intelligence 19, no. 1 (December 9, 2004): 69–98. http://dx.doi.org/10.1080/08839510590887414.

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18

Fu Guoyao. "Optimization methods for fuzzy clustering." Fuzzy Sets and Systems 93, no. 3 (February 1998): 301–9. http://dx.doi.org/10.1016/s0165-0114(96)00227-8.

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19

Chen, Toly, and Mao-Jiun J. Wang. "Forecasting methods using fuzzy concepts." Fuzzy Sets and Systems 105, no. 3 (August 1999): 339–52. http://dx.doi.org/10.1016/s0165-0114(97)00265-0.

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20

Li, Zhenquan. "Suitability of fuzzy reasoning methods." Fuzzy Sets and Systems 108, no. 3 (December 1999): 299–311. http://dx.doi.org/10.1016/s0165-0114(97)00296-0.

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21

Di Martino, Ferdinando, Irina Perfilieva, and Salvatore Sessa. "Fuzzy Methods for Data Analysis." Advances in Fuzzy Systems 2015 (2015): 1. http://dx.doi.org/10.1155/2015/957856.

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22

Oganesyants, Lev, Vladislav Semipyatniy, Aram Galstyan, Ramil Vafin, Sergey Khurshudyan, and Anastasia Ryabova. "Multi-criteria food products identification by fuzzy logic methods." Foods and Raw Materials 8, no. 1 (February 26, 2020): 12–19. http://dx.doi.org/10.21603/2308-4057-2020-1-12-19.

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The paper deals with the theory of fuzzy sets as applied to food industry products. The fuzzy indicator function is shown as a criterion for determining the properties of the product. We compared the approach of fuzzy and probabilistic classifiers, their fundamental differences and areas of applicability. As an example, a linear fuzzy classifier of the product according to one-dimensional criterion was given and an algorithm for its origination as well as approximation is considered, the latter being sufficient for the food industry for the most common case with one truth interval where the indicator function takes the form of a trapezoid. The results section contains exhaustive, reproducible, sequentially stated examples of fuzzy logic methods application for properties authentication and group affiliation of food products. Exemplified by measurements of the criterion with an error, we gave recommendations for determining the boundaries of interval identification for foods of mixed composition. Harrington’s desirability function is considered as a suitable indicator function of determining deterioration rate of a food product over time. Applying the fuzzy logic framework, identification areas of a product for the safety index by the time interval in which the counterparty selling this product should send it for processing, hedging their possible risks connected with the expiry date expand. In the example of multi-criteria evaluation of a food product consumer attractiveness, Harrington’s desirability function, acting as a quality function, was combined with Weibull probability density function, accounting for the product’s taste properties. The convex combination of these two criteria was assumed to be the decision-making function of the seller, by which identification areas of the food product are established.

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23

You, Cuilian, Yan Cheng, and Hongyan Ma. "Stability of Euler Methods for Fuzzy Differential Equation." Symmetry 14, no. 6 (June 20, 2022): 1279. http://dx.doi.org/10.3390/sym14061279.

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The Liu process is a fuzzy process whose membership function is a symmetric function on an expected value. The object of this paper was a fuzzy differential equation driven by Liu process. Since the existing fuzzy Euler solving methods (explicit Euler scheme, semi-implicit Euler scheme, and implicit Euler scheme) have the same convergence, to compare them, we presented four stabilities, i.e., asymptotical stability, mean square stability, exponential stability, and A stability. By choosing special fuzzy differential equation as a test equation, we deduced that mean square stability is equivalent to exponential stability. Furthermore, an explicit fuzzy Euler scheme and semi-implicit fuzzy Euler scheme showed asymptotical stability and mean square stability, while an explicit fuzzy Euler scheme failed to meet A stability but that an implicit fuzzy Euler scheme is A stable, and whether semi-implicit fuzzy Euler scheme is A stable depends on the values of α and λ.

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24

Mahajan, Sumati, and S. K. Gupta. "Methods to solve QPPs with fuzzy parameters and fuzzy variables." Journal of Intelligent & Fuzzy Systems 37, no. 2 (September 9, 2019): 2757–67. http://dx.doi.org/10.3233/jifs-18692.

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25

Lin, Lin, Xue-Hai Yuan, and Zun-Quan Xia. "Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy sets." Journal of Computer and System Sciences 73, no. 1 (February 2007): 84–88. http://dx.doi.org/10.1016/j.jcss.2006.03.004.

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26

ÇALIK, Ahmet. "Resilient Supplier Selection Based on Fuzzy AHP-Fuzzy ARAS Methods." İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi 9, no. 2 (October 30, 2022): 275–96. http://dx.doi.org/10.17336/igusbd.798775.

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Tedarikçilerin başarısı tüm tedarik zincirinin başarısını etkilediğinden tedarik zincirlerinde dış risklerin esas kaynağı tedarikçiler olmaktadır. Tedarikçilerin riskleri yönetme ve belirsiz durumlarla başa çıkma yeteneği, tedarik zincirinin dayanıklılığını artıracaktır. Artan ve farklılaşan bir rekabet ortamında tedarikçi seçimi, karar vericilerin en iyi sonucu elde etmesi için nicel ve nitel çoklu kriterleri dikkate almalarını gerektiren karmaşık bir süreçtir. Bu çalışmanın amacı, tekstil sektöründe dayanıklı tedarikçi seçimi için yeni bir çok kriterli bir karar verme (ÇKKV) yaklaşımı önermektir. İlk aşamada, tedarik zincirinin dayanıklılığını etkileyen kriterler uzman görüşü kullanılarak tanımlanmıştır. Bulanık küme teorisi belirsizliği daha iyi anlamamıza ve daha iyi tahmin etmemize yardımcı olduğu için, tanımlanan kriterlerin ağırlığını belirlemek için Bulanık Analitik Hiyerarşi Süreci (BAHP) ve tedarikçileri sıralamak için Bulanık Additive Ratio ASsessment (BARAS) kullanılmıştır. Önerilen ÇKKV yaklaşımının etkililiğini göstermek için tekstil sektöründeki bir firma için gerçek bir örnek olay uygulaması yapılmıştır. Bulgular, dayanıklı tedarikçi seçiminde en önemli faktörün dayanıklılık olduğunu ve bu faktör içerisinde tedarikçinin esnekliği ve cevap verilebilirlik alt kriterlerinin en önemli olduğunu göstermektedir. Bu araştırmanın sonuçları, tekstil sektöründeki en doğru tedarikçileri belirlemek için uygun yöntemleri belirleme ve uygulama konusunda araştırmacılara ve karar vericilere yardımcı olacaktır.

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27

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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28

HÜLLERMEIER, EYKE. "NUMERICAL METHODS FOR FUZZY INITIAL VALUE PROBLEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 05 (October 1999): 439–61. http://dx.doi.org/10.1142/s0218488599000404.

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In this paper, fuzzy initial value problems for modelling aspects of uncertainty in dynamical systems are introduced and interpreted from a probabilistic point of view. Due to the uncertainty incorporated in the model, the behavior of dynamical systems modelled in this way will generally not be unique. Rather, we obtain a large set of trajectories which are more or less compatible with the description of the system. We propose so-called fuzzy reachable sets for characterizing the (fuzzy) set of solutions to a fuzzy initial value problem. Loosely spoken, a fuzzy reachable set is defined as the (fuzzy) set of possible system states at a certain point of time, with given constraints concerning the initial system state and the system evolution. The main-part of the paper is devoted to the development of the numerical methods for the approximation of such sets. Algorithms for precise as well as outer approximations are presented. It is shown that fuzzy reachable sets can be approximated to any degree of accuracy under certain assumptions. Our method is illustrated by means of an example from the field of economics.

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29

ABDALLA, AREEG, and JAMES BUCKLEY. "MONTE CARLO METHODS IN FUZZY NON-LINEAR REGRESSION." New Mathematics and Natural Computation 04, no. 02 (July 2008): 123–41. http://dx.doi.org/10.1142/s1793005708000982.

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We apply our new fuzzy Monte Carlo method to certain fuzzy non-linear regression problems to estimate the best solution. The best solution is a vector of triangular fuzzy numbers, for the fuzzy coefficients in the model, which minimizes an error measure. We use a quasi-random number generator to produce random sequences of these fuzzy vectors which uniformly fill the search space. We consider example problems to show that this Monte Carlo method obtains solutions comparable to those obtained by an evolutionary algorithm.

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30

Kizielewicz, Bartłomiej, and Aleksandra Bączkiewicz. "Comparison of Fuzzy TOPSIS, Fuzzy VIKOR, Fuzzy WASPAS and Fuzzy MMOORA methods in the housing selection problem." Procedia Computer Science 192 (2021): 4578–91. http://dx.doi.org/10.1016/j.procs.2021.09.236.

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31

Pérez-Cañedo, Boris, José Luis Verdegay, Eduardo René Concepción-Morales, and Alejandro Rosete. "Lexicographic Methods for Fuzzy Linear Programming." Mathematics 8, no. 9 (September 9, 2020): 1540. http://dx.doi.org/10.3390/math8091540.

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Fuzzy Linear Programming (FLP) has addressed the increasing complexity of real-world decision-making problems that arise in uncertain and ever-changing environments since its introduction in the 1970s. Built upon the Fuzzy Sets theory and classical Linear Programming (LP) theory, FLP encompasses an extensive area of theoretical research and algorithmic development. Unlike classical LP, there is not a unique model for the FLP problem, since fuzziness can appear in the model components in different ways. Hence, despite fifty years of research, new formulations of FLP problems and solution methods are still being proposed. Among the existing formulations, those using fuzzy numbers (FNs) as parameters and/or decision variables for handling inexactness and vagueness in data have experienced a remarkable development in recent years. Here, a long-standing issue has been how to deal with FN-valued objective functions and with constraints whose left- and right-hand sides are FNs. The main objective of this paper is to present an updated review of advances in this particular area. Consequently, the paper briefly examines well-known models and methods for FLP, and expands on methods for fuzzy single- and multi-objective LP that use lexicographic criteria for ranking FNs. A lexicographic approach to the fuzzy linear assignment (FLA) problem is discussed in detail due to the theoretical and practical relevance. For this case, computer codes are provided that can be used to reproduce results presented in the paper and for practical applications. The paper demonstrates that FLP that is focused on lexicographic methods is an active area with promising research lines and practical implications.

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32

Сатторов and F. Sattorov. "Methods of indistinct multicriteria decision support in network planning." Forestry Engineering Journal 4, no. 2 (June 10, 2014): 0. http://dx.doi.org/10.12737/4534.

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In this paper we consider the solution of multicriteria decision support in the assessment of the time parameter of network plan under uncertainty fuzzy character. Proposed method is based on the mechanisms of fuzzy set theory and multicriteria optimization and represents a fuzzy model, as input parameters of which set of fuzzy criterion act, the calculation in a fuzzy model is carried out on the bases of fuzzy reasoning (logical implication) of the base of rules, and as an output parameter of model, ie, possibilistic duration of work acts as the resulting function.

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33

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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34

HERRERA, F., J. L. VERDEGAY, and M. KOVÁCS. "HOMOGENEOUS LINEAR FUZZY FUNCTIONS AND RANKING METHODS IN FUZZY LINEAR PROGRAMMING PROBLEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 01 (March 1994): 25–35. http://dx.doi.org/10.1142/s0218488594000043.

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A general model for Fuzzy Linear Programming problem is studied. Fuzzy numbers generated by an homogeneous linear fuzzy function have been used for representing the imprecision of the parameters. A solution method is proposed using fuzzy numbers ranking procedures.

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35

Miller, David J., Carl A. Nelson, Molly Boeka Cannon, and Kenneth P. Cannon. "Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data." Applied Computational Intelligence and Soft Computing 2009 (2009): 1–16. http://dx.doi.org/10.1155/2009/876361.

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Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzyα-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that theα-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

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36

Pełka, Marcin, and Andrzej Dudek. "The Comparison of Fuzzy Clustering Methods for Symbolic Interval-Valued Data." Przegląd Statystyczny 62, no. 3 (September 30, 2015): 301–19. http://dx.doi.org/10.5604/01.3001.0014.1755.

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Interval-valued data can find their practical applications in such situations as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. The primary objective of the presented paper is to compare three different methods of fuzzy clustering for interval-valued symbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering and fuzzy k-means clustering with fuzzy spectral clustering. Fuzzy spectral clustering combines both spectral and fuzzy approaches in order to obtain better results (in terms of Rand index for fuzzy clustering). The conducted simulation studies with artificial and real data sets confirm both higher usefulness and more stable results of fuzzy spectral clustering method, as compared to other existing fuzzy clustering methods for symbolic interval-valued data, when dealing with data featuring different cluster structures, noisy variables and/or outliers.

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37

Luo, Minxia, Wenling Li, and Hongyan Shi. "The Relationship between Fuzzy Reasoning Methods Based on Intuitionistic Fuzzy Sets and Interval-Valued Fuzzy Sets." Axioms 11, no. 8 (August 20, 2022): 419. http://dx.doi.org/10.3390/axioms11080419.

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Two important basic inference models of fuzzy reasoning are Fuzzy Modus Ponens (FMP) and Fuzzy Modus Tollens (FMT). In order to solve FMP and FMT problems, the full implication triple I algorithm, the reverse triple I algorithm and the Subsethood Inference Subsethood (SIS for short) algorithm are proposed, respectively. Furthermore, the existing reasoning algorithms are extended to intuitionistic fuzzy sets and interval-valued fuzzy sets according to different needs. The purpose of this paper is to study the relationship between intuitionistic fuzzy reasoning algorithms and interval-valued fuzzy reasoning algorithms. It is proven that there is a bijection between the solutions of intuitionistic fuzzy triple I algorithm and the interval-valued fuzzy triple I algorithm. Then, there is a bijection between the solutions of intuitionistic fuzzy reverse triple I algorithm and the interval-valued fuzzy reverse triple I algorithm. At the same time, it is shown that there is also a bijection between the solutions of intuitionistic fuzzy SIS algorithm and interval-valued fuzzy SIS algorithm.

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38

Sugak, Vladimir G., Ekaterina A. Mikhajlyuk, Alexander N. Dubovitskiy, Evgeny M. Mamatov, and Mikhail E. Mamatov. "Fuzzy logic methods in georadar applications." Экономика. Информатика 48, no. 1 (2021): 168–77. http://dx.doi.org/10.52575/2687-0932-2021-48-1-168-177.

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39

Li, Qun Xia. "Defuzzification Methods for Fuzzy Inventory Model." Advanced Materials Research 479-481 (February 2012): 399–402. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.399.

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This paper presents the defuzzification methods which are used in the fuzzy inventory model. The properties of the different defuzzification methods are investigated and derived. The results from the theatrical view are interesting.

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40

KumarMalhotra, Virender, Harleen Kaur, and M. Afshar Alam. "An Analysis of Fuzzy Clustering Methods." International Journal of Computer Applications 94, no. 19 (May 23, 2014): 9–12. http://dx.doi.org/10.5120/16497-6578.

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41

Russo, F., and G. Ramponi. "Fuzzy methods for multisensor data fusion." IEEE Transactions on Instrumentation and Measurement 43, no. 2 (April 1994): 288–94. http://dx.doi.org/10.1109/19.293435.

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Yager, Ronald R. "Fuzzy logic methods in recommender systems." Fuzzy Sets and Systems 136, no. 2 (June 2003): 133–49. http://dx.doi.org/10.1016/s0165-0114(02)00223-3.

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O. Chang, Yun-Hsi, and Bilal M. Ayyub. "Fuzzy regression methods – a comparative assessment." Fuzzy Sets and Systems 119, no. 2 (April 2001): 187–203. http://dx.doi.org/10.1016/s0165-0114(99)00091-3.

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Wu, Xiao-Hong, and Jian-Jiang Zhou. "Fuzzy discriminant analysis with kernel methods." Pattern Recognition 39, no. 11 (November 2006): 2236–39. http://dx.doi.org/10.1016/j.patcog.2006.05.004.

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D’Urso, Pierpaolo, and María Ángeles Gil. "Fuzzy Statistical Analysis: methods and applications." METRON 71, no. 3 (October 29, 2013): 197–99. http://dx.doi.org/10.1007/s40300-013-0029-5.

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Hongxing, Li. "Fuzzy clustering methods based on perturbation." Fuzzy Sets and Systems 33, no. 3 (December 1989): 291–302. http://dx.doi.org/10.1016/0165-0114(89)90119-x.

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Yuan, Yufei. "Criteria for evaluating fuzzy ranking methods." Fuzzy Sets and Systems 43, no. 2 (September 1991): 139–57. http://dx.doi.org/10.1016/0165-0114(91)90073-y.

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Sousa, J. M. C., J. M. Gil, C. S. Ribeiro, and J. R. Caldas Pinto. "Old document recognition using fuzzy methods." International Journal of Intelligent Systems Technologies and Applications 1, no. 3/4 (2006): 263. http://dx.doi.org/10.1504/ijista.2006.009908.

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Muren, Zhanxin Ma, and Wei Cui. "Generalized fuzzy data envelopment analysis methods." Applied Soft Computing 19 (June 2014): 215–25. http://dx.doi.org/10.1016/j.asoc.2014.02.014.

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Danesh, Sedigheh. "Fuzzy Parameters Estimation via Hybrid Methods." Hacettepe Journal of Mathematics and Statistics 47, no. 146 (September 19, 2016): 1. http://dx.doi.org/10.15672/hjms.201614621831.

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