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Journal articles on the topic 'Fuzzy Methods'

Author: Grafiati

Published: 10 March 2023

Last updated: 25 July 2025

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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 (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

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 (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 pro

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5

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

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6

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

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7

Ramzipoor, Roxanna. "Set theory and fuzzy sets: Their relationship to natural language." Qualitative & Multi-Method Research 12, no. 1 (2014): 9–14. https://doi.org/10.5281/zenodo.894606.

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An Interview with George Lakoff. Background: A recurring argument of scholars who advocate set theory and fuzzy sets for social science is that this frame-work is valuable and appropriate in part because it reflects the structure of meaning in natural language.2 George Lakoff has written extensively on these topics and is cited by these schol-ars as an authority. In this interview, Lakoff synthesizes a large body of research in linguistics and cognitive science, which contends that natural language is not set-theoretic in struc-ture. He also explores Lotfi Zadeh’s fuzzy logic, emphasizing both

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8

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 (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 functio

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9

Ali, Fareed Jameel, H. Saleh Hafed, Azmi Amirah, Alomari Abedel-Karrem, Ratib Anakira Nidal, and Haji Man Noraziah. "Efficient approximate analytical methods for nonlinear fuzzy boundary value problem." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1916–28. https://doi.org/10.11591/ijece.v12i2.pp1916-1928.

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This paper aims to solve the nonlinear two-point fuzzy boundary value problem (TPFBVP) using approximate analytical methods. Most fuzzy boundary value problems cannot be solved exactly or analytically. Even if the analytical solutions exist, they may be challenging to evaluate. Therefore, approximate analytical methods may be necessary to consider the solution. Hence, there is a need to formulate new, efficient, more accurate techniques. This is the focus of this study: two approximate analytical methods-homotopy perturbation method (HPM) and the variational iteration method (VIM) is proposed.

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10

Mahoney, James. "Reflections on fuzzy-set/QCA." Qualitative & Multi-Method Research 2, no. 2 (2004): 17–21. https://doi.org/10.5281/zenodo.998220.

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Ragin and Rihoux offer an informative overview of qualitative comparative analysis and its fuzzy-set extension (fs/QCA). They frame their discussion around three issues: (1) the distinctiveness of fs/QCA; (2) the state of the art; and (3) best practices. I will follow their lead by organizing this short note around these issues. However, whereas Ragin and Rihoux focus mostly on best practices, I will focus mostly on the distinctiveness of fs/QCA.

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11

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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12

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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13

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

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14

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

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15

Lodwick, Weldon A., and K. David Jamison. "Interval Methods and Fuzzy Optimization." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 03 (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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16

ABDALLA, AREEG, and JAMES BUCKLEY. "MONTE CARLO METHODS IN FUZZY GAME THEORY." New Mathematics and Natural Computation 03, no. 02 (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 te

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17

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,

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18

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 (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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19

You, Cuilian, Yan Cheng, and Hongyan Ma. "Stability of Euler Methods for Fuzzy Differential Equation." Symmetry 14, no. 6 (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 equivale

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20

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

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21

Su, Pan, and Xueying Ren. "Fuzzy Rule Interpolation Methods Based on Sparse Rule Bases." International Journal of Computer Science and Information Technology 5, no. 3 (2025): 83–91. https://doi.org/10.62051/ijcsit.v5n3.08.

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Fuzzy rule interpolation algorithms have broad applications in computational fuzzy inference systems. This paper systematically introduces interpolation methods based on α-cuts. It focuses on two classical α-cut-based interpolation methods: the KH fuzzy rule interpolation method and the Lagrange fuzzy rule interpolation method. Through theoretical analysis and comparative studies, the fundamental principles, performance characteristics, and limitations of these two interpolation algorithms are explored in depth. Based on this, a fuzzy inference system for the "tip calculation problem" was cons

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22

Kannagi, P., and G. Uthra. "Group Replacement Strategy under Fuzzy Methods." International Journal of Engineering and Advanced Technology 9, no. 1s5 (2019): 250–54. http://dx.doi.org/10.35940/ijeat.a1060.1291s519.

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Intuitionistic Fuzzy Numbers play an active role in finding an optimal solution for replacement problems under vague and uncertain situations. This paper gives a group replacement policy under fuzzy environment. Here all the costs and the number of units are taken as Triangular Intuitionistic Fuzzy Numbers (TIFNs). An example is used for illustration of the policy.

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23

Rahman, N. A., N. Rahim, R. Idris, and L. Abdullah. "Some Defuzzification Methods for Interval Type-2 Pentagonal Fuzzy Numbers." Malaysian Journal of Mathematical Sciences 18, no. 2 (2024): 343–56. http://dx.doi.org/10.47836/mjms.18.2.08.

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Interval type-2 pentagonal fuzzy numbers are developed from the pentagonal fuzzy numbers and interval-valued pentagonal fuzzy numbers concepts. Previous researchers have suggested that various defuzzification methods were used to transform pentagonal fuzzy numbers into crisp numbers. However, very little research discusses defuzzification methods for interval type-2 pentagonal fuzzy numbers. Five interval-tuple fuzzy numbers that act as the input transformed information are needed to obtain crisp numbers via defuzzification methods. Therefore, this study examined some defuzzification methods f

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24

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 (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 in

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25

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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26

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

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27

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

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28

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

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29

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

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30

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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31

ABDALLA, AREEG, and JAMES BUCKLEY. "MONTE CARLO METHODS IN FUZZY NON-LINEAR REGRESSION." New Mathematics and Natural Computation 04, no. 02 (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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32

HÜLLERMEIER, EYKE. "NUMERICAL METHODS FOR FUZZY INITIAL VALUE PROBLEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 05 (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

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Сатторов and F. Sattorov. "Methods of indistinct multicriteria decision support in network planning." Forestry Engineering Journal 4, no. 2 (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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Agayan, Sergey M., Dmitriy A. Kamaev, Shamil R. Bogoutdinov, Andron O. Aleksanyan, and Boris V. Dzeranov. "Time Series Analysis by Fuzzy Logic Methods." Algorithms 16, no. 5 (2023): 238. http://dx.doi.org/10.3390/a16050238.

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The method of analyzing data known as Discrete Mathematical Analysis (DMA) incorporates fuzzy mathematics and logic. This paper focuses on applying DMA to study the morphology of time series by utilizing the language of fuzzy mathematics. The morphological characteristics of the time series, such as background, slopes, and vertices, are considered fuzzy sets within the domain of its definition. This allows for the use of fuzzy logic in examining the morphology of time series, ultimately leading to the detection of anomalies.

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35

Ramesh, R., S. Krishnaprakash, Nikola Ivkovic, Mario Konecki, and Chiranjibe Jana. "Spherical Picture Fuzzy Sets: Enhancing Decision-Making with Geometric Bonferroni Methods." International Journal of Analysis and Applications 23 (June 9, 2025): 135. https://doi.org/10.28924/2291-8639-23-2025-135.

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The representation of a spherical picture fuzzy set (SPFS) employs a spherical framework to depict uncertainty across positive, negative, and neutral membership functions, effectively capturing the vagueness inherent in these degrees. Through this structure, SPFS enable nuanced decision-making, supported by novel ranking mechanisms, parametric distance measures and Euclidean distance evaluations. Additionally, an extended version of the spherical picture fuzzy Bonferroni method is introduced, tailored to MCDM scenarios. Applied to diverse stakeholder contexts, this approach overcomes the limit

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36

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 (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 di

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37

Wai, Wai Tun. "Comparison of Defuzzification Methods from a Real World Problem." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2194–97. https://doi.org/10.5281/zenodo.3591212.

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Fuzzy logic is a method to formalize the human capacity of imprecise reasoning. Fuzzy logic react how people think. It tries to make our sense of words and our decision making. As a result, it is making to human intelligent systems. There are three basic steps for fuzzy logic system i.e. Fuzzification, Rule evaluation and Defuzzification. In this paper I discuss a real life problem in which different results of same survey were presented. The survey data represent vague values in the form of fuzzy sets. To solve this problem we applied defuzzification methods to get the best result from survey

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38

Pełka, Marcin, and Andrzej Dudek. "The Comparison of Fuzzy Clustering Methods for Symbolic Interval-Valued Data." Przegląd Statystyczny 62, no. 3 (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). T

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39

Heidarova, Olena. "APPLICATION OF FUZZY SET METHODS FOR MAKING MANAGEMENT DECISIONS IN CRISIS CONDITIONS." Herald of Khmelnytskyi National University. Economic sciences 318, no. 3 (2023): 118–23. http://dx.doi.org/10.31891/2307-5740-2023-318-3-17.

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The possibility of forecasting the results of management decision-making using the methods of linguistic vector evaluations, fuzzy logical conclusion, fuzzy relation of preferences, maximin convolution is considered. The method of applying the methods of linguistic vector evaluations, fuzzy logical conclusion, fuzzy relation of advantages, maximum convolution evaluation of possible values of management decisions in conditions of uncertainty (crisis). It was determined that the application of this method, with a simpler mathematical apparatus, allows to evaluate the possible variants of the val

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40

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

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41

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 (2007): 84–88. http://dx.doi.org/10.1016/j.jcss.2006.03.004.

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42

ÇALIK, Ahmet. "Resilient Supplier Selection Based on Fuzzy AHP-Fuzzy ARAS Methods." İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi 9, no. 2 (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 önerme

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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

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44

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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45

Ercan-Teksen, Hatice, and Ahmet Sermet Anagün. "Intuitionistic fuzzy c-control charts using defuzzification and likelihood methods." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6465–73. http://dx.doi.org/10.3233/jifs-189110.

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Control chart is one of the statistical methods to analyze the process. The use of fuzzy sets in control charts, which are divided into qualitative and quantitative data, has been applied in many studies recently. Especially for qualitative control charts, data collection is more difficult and more subjective. Therefore, fuzzy sets are used to reduce losses in data. There are many control chart studies created by type-1 fuzzy sets available in the literature. In recent years, examples of fuzzy control charts with extensions of fuzzy sets have been found. The aim of this study is to obtain c-co

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Zimwara, Davison, Lameck Mugwagwa, and Knowledge Nherera. "Soft Computing Methods for Predicting Environmental Quality." Journal of US-China Public Administration 10, no. 4 (2013): 345–57. https://doi.org/10.5281/zenodo.2551037.

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Sugarcane growing and processing is associated with environmental degradation and pollution. The impact that sugar processing industries have on the environment affects the ecosystem. Methods of soft computing that is fuzzy logic, neural networks, and genetic algorithms can be adopted for environmental protection, particularly in the developing countries. Soft computing techniques, particularly neural networks and fuzzy logic, have been used to predict and sometimes control air quality. This paper looks at how fuzzy logic can be adopted for predicting air quality. The common environmental impa

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47

Pérez-Cañedo, Boris, José Luis Verdegay, Eduardo René Concepción-Morales, and Alejandro Rosete. "Lexicographic Methods for Fuzzy Linear Programming." Mathematics 8, no. 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 method

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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 ver

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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 (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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Repnik, Maja. "THE APPLICATION OF FUZZY LINEAR PROGRAMMING METHODS IN ENERGY PLANNING." Journal of Energy Technology 9, no. 4 (2024): 35–42. https://doi.org/10.18690/jet.9.4.35-42.2016.

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Very frequently some of the parameters of certain optimization problems solvable by linear programing cannot be precisely determined. In this case, fuzzy linear programming techniques can be used. In the relevant literature, there are many real-world problems solved by fuzzy linear programming methods, but they are scarce in the field of energy technology. The main reason for that might be the lack of effective software for solving fuzzy linear programming problems. In 2015, researchers developed the R package FuzzyLP, software for public use, which solves fuzzy linear programming problems dir

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