Relevant bibliographies by topics / Method of fuzzy sets

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Method of fuzzy sets'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Method of fuzzy sets"

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Karnaukhov, V. M. "Adaptive method of fuzzy sets." Otkrytoe i distantsionnoe obrazovanie, no. 4(68) (December 1, 2017): 63–69. http://dx.doi.org/10.17223/16095944/68/10.

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Sulaiman, Raden, Yuliani Puji Astuti, Dwi Nur Yunianti, and N. Azzah Awang. "On the Picture Fuzzy-TOPSIS Method." European Journal of Pure and Applied Mathematics 17, no. 3 (2024): 1727–36. http://dx.doi.org/10.29020/nybg.ejpam.v17i3.5225.

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Research related to the Technique for Order Preference by Similarity to Ideal Solotion (TOPSIS) method have been developed using fuzzy or intuitionistic fuzzy sets. In this article, we provide the development of the topsis method based on the generalization of fuzzy sets. We present a new method of topsis based on picture fuzzy sets. In this article, we propose steps of TOPSIS picture fuzzy sets method. Finally, we presented the illustrative example of this method.
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Kutlu Gündoğdu, Fatma, and Cengiz Kahraman. "Spherical fuzzy sets and spherical fuzzy TOPSIS method." Journal of Intelligent & Fuzzy Systems 36, no. 1 (2019): 337–52. http://dx.doi.org/10.3233/jifs-181401.

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Karnaukhov, V. M. "Non-ADAPTIVE METHOD OF FUZZY SETS." Otkrytoe i distantsionnoe obrazovanie, no. 3(67) (September 1, 2017): 57–64. http://dx.doi.org/10.17223/16095944/67/9.

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Eğrioğlu, Erol. "A New Time-Invariant Fuzzy Time Series Forecasting Method Based on Genetic Algorithm." Advances in Fuzzy Systems 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/785709.

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In recent years, many fuzzy time series methods have been proposed in the literature. Some of these methods use the classical fuzzy set theory, which needs complex matricial operations in fuzzy time series methods. Because of this problem, many studies in the literature use fuzzy group relationship tables. Since the fuzzy relationship tables use order of fuzzy sets, the membership functions of fuzzy sets have not been taken into consideration. In this study, a new method that employs membership functions of fuzzy sets is proposed. The new method determines elements of fuzzy relation matrix bas
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Phong, Phạm Đình, Nguyen Duc Du, Nguyen Thanh Thuy, and Hoang Van Thong. "A HEDGE ALGEBRAS BASED CLASSIFICATION REASONING METHOD WITH MULTI-GRANULARITY FUZZY PARTITIONING." Journal of Computer Science and Cybernetics 35, no. 4 (2019): 319–36. http://dx.doi.org/10.15625/1813-9663/35/4/14348.

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During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to
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Aydoğmuş, Hacer Yumurtacı, Eren Kamber, and Cengiz Kahraman. "ERP selection using picture fuzzy CODAS method." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11363–73. http://dx.doi.org/10.3233/jifs-202564.

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The purpose of this study is to develop an extension of CODAS method using picture fuzzy sets. In this respect, a new methodology is introduced to figure out how picture fuzzy numbers can be applied to CODAS method. COmbinative Distance-based Assessment (CODAS) is a new MCDM method proposed by Ghorabaee et al. Picture fuzzy sets (PFSs) are a new extension of ordinary fuzzy sets for representing human’s judgments having possibility more than two answers such as yes, no, refusal and neutral. Compared with other studies, the proposed method integrates multi-criteria decision analysis with picture
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S., SANDHIYA. "A Decision-Making Method based on Moora Under Fuzzy Soft Sets." Journal of Research on the Lepidoptera 51, no. 2 (2020): 720–29. http://dx.doi.org/10.36872/lepi/v51i2/301130.

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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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Klen, André Monteiro, and Milene Sabino Lana. "Fuzzy Algorithm of discontinuity sets." Rem: Revista Escola de Minas 67, no. 4 (2014): 439–45. http://dx.doi.org/10.1590/0370-44672014670178.

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The clustering of discontinuity sets is not always a trivial task, especially when only the pole density diagram is used, the classical method. This process is extremely subjective since the size of the counting circle, the pole overlapping, and the presence of outliers between families make it difficult to define their characteristics. In these cases, it is useful to apply numerical and classical methods together. For that, this article proposes an algorithm based on the Fuzzy K-means method that allows the clustering of the discontinuities without the influence of these factors. The algorith
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Dissertations / Theses on the topic "Method of fuzzy sets"

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Khoshnoud, Farbod. "A novel modal analysis method based on fuzzy sets." Thesis, Brunel University, 2005. http://bura.brunel.ac.uk/handle/2438/380.

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A novel method of vibration modelling is proposed in this thesis. This method involves estimating the mode shapes of a general structure and describing these shapes in terms of fuzzy membership functions. These estimations or initial guesses are based on engineer's experience or physical insight into natural mode shapes assisted by end and boundary conditions and some rules. The guessed mode shapes were referred to as Mode Shape Forms (MSFs). MSFs are approximate mode shapes, therefore there are uncertainties involve with their values where this uncertainty is expressed by fuzzy sets. The defl
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Swartz, Andre Michael. "Methods for designing and optimizing fuzzy controllers." Thesis, Rhodes University, 2000. http://hdl.handle.net/10962/d1005226.

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We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work.
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McCulloch, Josie C. "Novel methods of measuring the similarity and distance between complex fuzzy sets." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33401/.

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This thesis develops measures that enable comparisons of subjective information that is represented through fuzzy sets. Many applications rely on information that is subjective and imprecise due to varying contexts and so fuzzy sets were developed as a method of modelling uncertain data. However, making relative comparisons between data-driven fuzzy sets can be challenging. For example, when data sets are ambiguous or contradictory, then the fuzzy set models often become non-normal or non-convex, making them difficult to compare. This thesis presents methods of comparing data that may be repre
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Maglaras, George K. "Experimental comparison of probabilistic methods and fuzzy sets for designing under uncertainty." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/37765.

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Recently, probabilistic methods have been used extensively to model uncertainty in many design optimization problems. An alternative approach for modeling uncertainties is fuzzy sets. Fuzzy sets usually require much less information than probabilistic methods and they rely on expert opinion. In principle, probability theory should work better in problems involving only random uncertainties, if sufficient information is available to model these uncertainties accurately. However, because such information is rarely available, probabilistic models rely on a number of assumptions regarding the magn
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Sladoje, Nataša. "On analysis of discrete spatial fuzzy sets in 2 and 3 dimensions /." Uppsala : Centre for Image Analysis, Swedish University of Agricultural Sciences, 2005. http://epsilon.slu.se/2005112.pdf.

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Bouchereau, Vivianne. "An integrated systems approach to QFD." Thesis, University of South Wales, 2000. https://pure.southwales.ac.uk/en/studentthesis/an-integrated-systems-approach-to-qfd(3699183a-e87f-4c11-9c04-65e0a1da7aae).html.

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This thesis reviews Quality Function Deployment (QFD) and its relation with the Total Quality Management philosophy. In particular the thesis focuses on the inherent drawbacks of QFD and it investigates potential techniques and methods that could be integrated with QFD to overcome some of its problems. Fuzzy Logic/Fuzzy Sets and the Taguchi Method are identified as techniques and methods to be incorporated within the QFD process to provide a more consistent, quantitative and rigorous method to analyse subjective data in the QFD charts. Two approaches are developed that integrate Fuzzy Logic an
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Gayer, Fernanda Almeida Marchini [UNESP]. "A Matemática está em tudo: modelagem fuzzy para um problema da indústria e uma proposta de aplicação no Ensino Médio." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/152274.

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Haluska, Lukáš. "Využití fuzzy clusteringu při modelování vlastností ztvrdlých betonů." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-233172.

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Compressive strength is one of the most monitored parameter of hardened concrete and it is usually determinated by destructive testing. Non-destructive testing applied on existing constructions uses regression analysis to estimate compressive strength. High variability of outputs and eventually failure of necessary requirements for using regression analysis complicate these methods. The aim of this thesis is to show posibility of using fuzzy clustering in this topic and to compare outputs with classic methods.
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Ljung, Patric. "Efficient Methods for Direct Volume Rendering of Large Data Sets." Doctoral thesis, Norrköping : Department of Science and Technology, Linköping University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7232.

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Demircioglu, Ersan. "A Novel Refinement Method For Automatic Image Annotation Systems." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613346/index.pdf.

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Image annotation could be defined as the process of assigning a set of content related words to the image. An automatic image annotation system constructs the relationship between words and low level visual descriptors, which are extracted from images and by using these relationships annotates a newly seen image. The high demand on image annotation requirement increases the need to automatic image annotation systems. However, performances of current annotation methods are far from practical usage. The most common problem of current methods is the gap between semantic words and low level visual
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Books on the topic "Method of fuzzy sets"

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Siegfried, Gottwald, ed. Fuzzy sets, fuzzy logic, fuzzy methods with applications. J. Wiley, 1995.

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Manton, Kenneth G. Statistical application using fuzzy sets. Wiley, 1994.

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United States. National Aeronautics and Space Administration., ed. Fuzzy set methods for object recognition in space applications: Second quarter report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1991.

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Keller, James M. Fuzzy set methods for object recognition in space applications: Third quarter report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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United States. National Aeronautics and Space Administration., ed. Fuzzy set methods for object recognition in space applications: Third quarter report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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Keller, James M. Fuzzy set methods for object recognition in space applications: Second quarter report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1991.

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Bloch, Isabelle, and Anca Ralescu. Fuzzy Sets Methods in Image Processing and Understanding. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19425-2.

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Ebrahimnejad, Ali, and José Luis Verdegay. Fuzzy Sets-Based Methods and Techniques for Modern Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73903-8.

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Akram, Muhammad, Shumaiza, and José Carlos Rodríguez Alcantud. Multi-criteria Decision Making Methods with Bipolar Fuzzy Sets. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0569-0.

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Miyamoto, Sadaaki. Algorithms for fuzzy clustering: Methods in c-means clustering with applications. Springer, 2008.

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Book chapters on the topic "Method of fuzzy sets"

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Peng, Xindong. "Pythagorean Fuzzy MCDM Method Based on CODAS." In Pythagorean Fuzzy Sets. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1989-2_11.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Fuzzy Sets." In Data Mining Methods for Knowledge Discovery. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_3.

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Buckley, James J., and Leonard J. Jowers. "Fuzzy Sets." In Monte Carlo Methods in Fuzzy Optimization. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76290-4_2.

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Şahin, Rıdvan. "COPRAS Method with Neutrosophic Sets." In Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00045-5_19.

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Jezek, Karel, and Martin Zíma. "Magic Sets Method with Fuzzy Logic." In Advances in Information Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36077-8_8.

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Frolov, Dmitry, Boris Mirkin, Susana Nascimento, and Trevor Fenner. "Method for Generalization of Fuzzy Sets." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20912-4_26.

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Gul, Muhammet, and Melih Yucesan. "A Risk Prioritization Method Based on Interval-Valued Pythagorean Fuzzy TOPSIS and Its Application for Prioritization of the Risks Emerged at Hospitals During the Covid-19 Pandemic." In Pythagorean Fuzzy Sets. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1989-2_6.

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Tsumoto, Shusaku, and Hiroshi Tanaka. "PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory." In Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3238-7_33.

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Yager, Ronald R. "OWA Aggregation of Probability Distributions Using the Probabilistic Exceedance Method." In Fuzzy Sets, Rough Sets, Multisets and Clustering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47557-8_16.

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Xiao, Jianfeng, Qun Liu, and Xin Huang. "A Method of Multi-USV Reward Design Using Fuzzy Control." In Rough Sets. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65665-1_19.

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Conference papers on the topic "Method of fuzzy sets"

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de Oliveira, Wesley R. "Study of a Condition Monitoring Method Based on the Concept of Complex Fuzzy Sets." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711633.

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Zhou, Xiaoguang, Quan YuFeng, Songwei Li, and Lijun Qi. "A new method to construct intuitionistic fuzzy sets deducing from fuzzy sets via fuzzy entropy." In 2014 5th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2014. http://dx.doi.org/10.1109/icsess.2014.6933581.

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Shen, Zixiao, Xin Chen, and Jonathan M. Garibaldi. "A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858890.

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Chengyuan Chen and Qiang Shen. "A new method for rule interpolation inspired by rough-fuzzy sets." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251216.

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Kok Chin Chai, Kai Meng Tay, and Chee Peng Lim. "A new Dempster-Shafer Theory-based method with fuzzy targets for Fuzzy Sets ranking." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622358.

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Zhang, Hailin, Yafei Song, Lei Lei, and Zhimin Qi. "A new method to measure the knowledge amount of Atanassov’s intuitionistic fuzzy sets." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177541.

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Zixin, Liu, Liu Xuesheng, Niu Mingming, and Gang Jiatai. "An ordering method of fuzzy sets based on rough fuzzy numbers." In 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). IEEE, 2014. http://dx.doi.org/10.1109/mfi.2014.6997695.

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Liu Jian, Shunxiang Wu, Zhizhan Liu, and Yongjian Zhang. "New method for approximating Vague Sets to Fuzzy Sets based on voting model." In 2008 IEEE Conference on Cybernetics and Intelligent Systems (CIS). IEEE, 2008. http://dx.doi.org/10.1109/iccis.2008.4670961.

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Wang, Xing-ju, Yu-wei Hao, and Ran-hang Zhao. "Method, Model and Application for the Conversion from Vague Sets to Fuzzy Sets." In 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009). IEEE, 2009. http://dx.doi.org/10.1109/aici.2009.397.

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Hu, Haiqing, Pitao Wang, and Kaiquan Shi. "Function S-Rough Sets Method in Feature Selection." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.204.

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Reports on the topic "Method of fuzzy sets"

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest
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Atanasso, Krassimir. Elliptic Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.06.02.

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Ismaili, Shpend, and Stefka Fidanova. Application of Intuitionistic Fuzzy Sets on Agent Based Modelling. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.06.12.

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Atanassov, Krassimir. A New Modal Type Operator over Intuitionistic Fuzzy Sets. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.08.01.

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Andonyadis, Manol, A. Altschaeffl, and Jean-Lou Chameau. The Use of Fuzzy Sets Mathematics to Assist Pavement Evaluation and Management. Purdue University, 1985. http://dx.doi.org/10.5703/1288284314099.

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Darby, John L. LinguisticBelief: a java application for linguistic evaluation using belief, fuzzy sets, and approximate reasoning. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/903427.

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Goodman, I. R. Applications of Random Set Representations of Fuzzy Sets to Determining Measures of Central Tendency. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada305661.

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Andonyadis, Manol, A. Altschaeffl, and Jean-Lou Chameau. The Use of Fuzzy Sets Mathematics to Assist Pavement Evaluation and Management: Executive Summary. Purdue University, 1986. http://dx.doi.org/10.5703/1288284314098.

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Osipov, Gennadij Sergeevich. The simplest method of solving a fuzzy linear problem EXCHANGE. КультИнформПресс, 2017. http://dx.doi.org/10.18411/spbcsa-2017-4.

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Bozhenyuk, Alexander V., Stanislav L. Belyakov, and Margarita V. Knyazeva. Method for Finding the Chromatic Number of Intuitionistic Fuzzy Graph. DOI CODE, 2023. http://dx.doi.org/10.18411/doicode-2023.214.

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