Gotowe bibliografie tematyczne / Fuzzy analysis

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Gotowa bibliografia na temat „Fuzzy analysis”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Artykuły w czasopismach na temat "Fuzzy analysis"

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

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

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Ameer, Eskandar, Hassen Aydi, and Muhammad Arshad. "On Fuzzy Fixed Points and an Application to Ordinary Fuzzy Differential Equations." Journal of Function Spaces 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/8835751.

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The aim of this paper is to obtain the common fuzz fixed points of α -fuzzy mappings satisfying generalized almost Y , Λ -contraction in complete metric spaces. Our results are extensions and improvements of the several well-known recent and classical results in literature. We give an example for supporting these results. As an application, we apply our obtained results to study the existence of a solution for a second order nonlinear boundary value problem.

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Ameen, Zanyar A., Tareq M. Al-shami, A. A. Azzam, and Abdelwaheb Mhemdi. "A Novel Fuzzy Structure: Infra-Fuzzy Topological Spaces." Journal of Function Spaces 2022 (April 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/9778069.

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Obtaining a weaker condition that preserves some inspired topological properties is always desirable. As a result, we introduce the concept of infra-fuzzy topology, which is a subset family that degrades the concept of fuzzy topology by omitting the condition of closedness under arbitrary unions. Fundamental properties of infra-fuzzy topological spaces are investigated, including infra-fuzzy open and infra-fuzzy closed sets, infra-fuzzy interior and infra-fuzzy closure operators, and the infra-fuzzy boundary of a fuzzy set. It is not possible to expect the latter concepts to have properties identical to those in ordinary fuzzy topological spaces. More precisely, the infra-fuzzy interior of a set need not be infra-fuzzy open, and the infra-fuzzy closure and boundary of a set may not be infra-fuzzy closed. Then, employing infra-fuzzy neighborhood systems, infra-fuzzy Q-neighborhood systems, the basis of infra-fuzzy topology, and infra-fuzzy relative topology, we propose several approaches for generating infra-fuzzy topologies. Finally, we define the notions of continuity, openness, closedness, and homeomorphism of mappings in the context of infra fuzziness and investigate some of their properties and characterizations. We show that the usual characterization of earlier notions in the infra-fuzzy structure is incorrect. We demonstrate that the family of all infra-fuzzy homeomorphisms on an infra-fuzzy topological space forms a group under mappings composition. We finish this work by proving that each infra-fuzzy homeomorphism between two infra-fuzzy topological spaces produces an isomorphism on groups of infra-fuzzy homeomorphisms of the corresponding spaces.

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Byrne, Peter. "Fuzzy analysis." Journal of Property Valuation and Investment 13, no. 3 (August 1995): 22–41. http://dx.doi.org/10.1108/14635789510088591.

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Madhuri, V., Omar Bazighifan, Ali Hasan Ali, and A. El-Mesady. "On Fuzzy F ∗ -Simply Connected Spaces in Fuzzy F ∗ -Homotopy." Journal of Function Spaces 2022 (April 28, 2022): 1–6. http://dx.doi.org/10.1155/2022/9926963.

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In this paper, the notions of fuzzy F ∗ -simply connected spaces and fuzzy F ∗ -structure homeomorphisms are introduced, and further fuzzy F ∗ -structure homeomorphism between fuzzy F ∗ -path-connected spaces are studied. Also, it is shown that every fuzzy F ∗ -structure subspace of fuzzy F ∗ -simply connected space is fuzzy F ∗ -simply connected subspace. Further, the concepts of fuzzy F ∗ -contractible spaces and fuzzy F ∗ -retracts are introduced, and it is proved that every fuzzy F ∗ -contractible space is fuzzy F ∗ -simply connected.

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Pedrycz, Witold. "From fuzzy data analysis and fuzzy regression to granular fuzzy data analysis." Fuzzy Sets and Systems 274 (September 2015): 12–17. http://dx.doi.org/10.1016/j.fss.2014.04.017.

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Papageorgiou, Nikolaos S. "Fuzzy topology and fuzzy multifunctions." Journal of Mathematical Analysis and Applications 109, no. 2 (August 1985): 397–425. http://dx.doi.org/10.1016/0022-247x(85)90159-3.

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Prevot, M. "Fuzzy goals under fuzzy constraints." Journal of Mathematical Analysis and Applications 118, no. 1 (August 1986): 180–93. http://dx.doi.org/10.1016/0022-247x(86)90302-1.

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Al-shami, Tareq M., Hariwan Z. Ibrahim, A. A. Azzam, and Ahmed I. EL-Maghrabi. "SR-Fuzzy Sets and Their Weighted Aggregated Operators in Application to Decision-Making." Journal of Function Spaces 2022 (March 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/3653225.

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An intuitionistic fuzzy set is one of the efficient generalizations of a fuzzy set for dealing with vagueness/uncertainties in information. Under this environment, in this manuscript, we familiarize a new type of extensions of fuzzy sets called square-root fuzzy sets (briefly, SR-Fuzzy sets) and contrast SR-Fuzzy sets with intuitionistic fuzzy sets and Pythagorean fuzzy sets. We discover the essential set of operations for the SR-Fuzzy sets along with their several properties. In addition, we define a score function for the ranking of SR-Fuzzy sets. To study multiattribute decision-making problems, we introduce four new weighted aggregated operators, namely, SR-Fuzzy weighted average (SR-FWA) operator, SR-Fuzzy weighted geometric (SR-FWG) operator, SR-Fuzzy weighted power average (SR-FWPA) operator, and SR-Fuzzy weighted power geometric (SR-FWPG) operator over SR-Fuzzy sets. We apply these operators to select the top-rank university and show how we can choose the best option by comparing the aggregate outputs through score values.

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Bhunia, Supriya, Ganesh Ghorai, Marwan Amin Kutbi, Muhammad Gulzar, and Md Ashraful Alam. "On the Algebraic Characteristics of Fuzzy Sub e-Groups." Journal of Function Spaces 2021 (October 20, 2021): 1–7. http://dx.doi.org/10.1155/2021/5253346.

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Fuzzy set is a modern tool for depicting uncertainty. This paper introduces the concept of fuzzy sub e-group as an extension of fuzzy subgroup. The concepts of identity and inverse are generalized in fuzzy sub e-groups. Every fuzzy subgroup is proven to be a fuzzy sub e-group, but the converse is not true. Various properties of fuzzy sub e-groups are established. Moreover, the concepts of proper fuzzy sub e-group and super fuzzy sub e-group are discussed. Further, the concepts of fuzzy e-coset and normal fuzzy sub e-group are presented. Finally, we describe the effect of e-group homomorphism on normal fuzzy sub e-groups.

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Rozprawy doktorskie na temat "Fuzzy analysis"

Karim, Ehsanul, Sri Phani Venkata Siva Krishna Madani, and Feng Yun. "Fuzzy Clustering Analysis." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2165.

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The Objective of this thesis is to talk about the usage of Fuzzy Logic in pattern recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that we choose to process the data is completely depends on the type of data. Pattern reorganization as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explaination of the process generating the data clarity seen and so on and so forth. With this basic school of thought we plunge into the world of Fuzzy Logic for the process of pattern recognition. Fuzzy Logic like any other mathematical field has its own set of principles, types, representations, usage so on and so forth. Hence our job primarily would focus to venture the ways in which Fuzzy Logic is applied to pattern recognition and knowledge of the results. That is what will be said in topics to follow. Pattern recognition is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. In the broadest sense, pattern recognition is any form of information processing for which both the input and output are different kind of data, medical records, aerial photos, market trends, library catalogs, galactic positions, fingerprints, psychological profiles, cash flows, chemical constituents, demographic features, stock options, military decisions.. Most pattern recognition techniques involve treating the data as a variable and applying standard processing techniques to it.

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Drobics, Mario. "Data analysis using fuzzy expressions /." Linz : Trauner, 2005. http://aleph.unisg.ch/hsgscan/hm00166742.pdf.

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Chan, Chee Seng. "Fuzzy qualitative human motion analysis." Thesis, University of Portsmouth, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494009.

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Human motion analysis is a very important task for computer vision with a spectrum of potential applications. This thesis presents a novel approach to the problem of human motion understanding. The main contribution of the thesis is that fuzzy qualitative description has been developed for studying human motion from image sequences.

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Yamazaki, Tsukasa. "An improved algorithm for a self-organising controllerd its experimental analysis." Thesis, Queen Mary, University of London, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320959.

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Reynolds, Robert. "Gene Expression Data Analysis Using Fuzzy Logic." Fogler Library, University of Maine, 2001. http://www.library.umaine.edu/theses/pdf/REynoldsR2001.pdf.

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Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.

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Popoola, Ademola Olayemi. "Fuzzy-wavelet method for time series analysis." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/804949/.

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Merilan, Jean Elizabeth 1962. "The Use of Fuzzy Analysis in Epidemiology." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/565573.

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De, Maio Carmen. "Fuzzy concept analysis for semantic knowledge extraction." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/1307.

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2010 - 2011<br>Availability of controlled vocabularies, ontologies, and so on is enabling feature to provide some added values in terms of knowledge management. Nevertheless, the design, maintenance and construction of domain ontologies are a human intensive and time consuming task. The Knowledge Extraction consists of automatic techniques aimed to identify and to define relevant concepts and relations of the domain of interest by analyzing structured (relational databases, XML) and unstructured (text, documents, images) sources. Specifically, methodology for knowledge extraction defined in this research work is aimed at enabling automatic ontology/taxonomy construction from existing resources in order to obtain useful information. For instance, the experimental results take into account data produced with Web 2.0 tools (e.g., RSS-Feed, Enterprise Wiki, Corporate Blog, etc.), text documents, and so on. Final results of Knowledge Extraction methodology are taxonomies or ontologies represented in a machine oriented manner by means of semantic web technologies, such as: RDFS, OWL and SKOS. The resulting knowledge models have been applied to different goals. On the one hand, the methodology has been applied in order to extract ontologies and taxonomies and to semantically annotate text. On the other hand, the resulting ontologies and taxonomies are exploited in order to enhance information retrieval performance and to categorize incoming data and to provide an easy way to find interesting resources (such as faceted browsing). Specifically, following objectives have been addressed in this research work:  Ontology/Taxonomy Extraction: that concerns to automatic extraction of hierarchical conceptualizations (i.e., taxonomies) and relations expressed by means typical description logic constructs (i.e., ontologies).  Information Retrieval: definition of a technique to perform concept-based the retrieval of information according to the user queries.  Faceted Browsing: in order to automatically provide faceted browsing capabilities according to the categorization of the extracted contents.  Semantic Annotation: definition of a text analysis process, aimed to automatically annotate subjects and predicates identified. The experimental results have been obtained in some application domains: e-learning, enterprise human resource management, clinical decision support system. Future challenges go in the following directions: investigate approaches to support ontology alignment and merging applied to knowledge management.<br>X n.s.

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Glodeanu, Cynthia Vera. "Conceptual Factors and Fuzzy Data." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-103775.

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With the growing number of large data sets, the necessity of complexity reduction applies today more than ever before. Moreover, some data may also be vague or uncertain. Thus, whenever we have an instrument for data analysis, the questions of how to apply complexity reduction methods and how to treat fuzzy data arise rather naturally. In this thesis, we discuss these issues for the very successful data analysis tool Formal Concept Analysis. In fact, we propose different methods for complexity reduction based on qualitative analyses, and we elaborate on various methods for handling fuzzy data. These two topics split the thesis into two parts. Data reduction is mainly dealt with in the first part of the thesis, whereas we focus on fuzzy data in the second part. Although each chapter may be read almost on its own, each one builds on and uses results from its predecessors. The main crosslink between the chapters is given by the reduction methods and fuzzy data. In particular, we will also discuss complexity reduction methods for fuzzy data, combining the two issues that motivate this thesis<br>Komplexitätsreduktion ist eines der wichtigsten Verfahren in der Datenanalyse. Mit ständig wachsenden Datensätzen gilt dies heute mehr denn je. In vielen Gebieten stößt man zudem auf vage und ungewisse Daten. Wann immer man ein Instrument zur Datenanalyse hat, stellen sich daher die folgenden zwei Fragen auf eine natürliche Weise: Wie kann man im Rahmen der Analyse die Variablenanzahl verkleinern, und wie kann man Fuzzy-Daten bearbeiten? In dieser Arbeit versuchen wir die eben genannten Fragen für die Formale Begriffsanalyse zu beantworten. Genauer gesagt, erarbeiten wir verschiedene Methoden zur Komplexitätsreduktion qualitativer Daten und entwickeln diverse Verfahren für die Bearbeitung von Fuzzy-Datensätzen. Basierend auf diesen beiden Themen gliedert sich die Arbeit in zwei Teile. Im ersten Teil liegt der Schwerpunkt auf der Komplexitätsreduktion, während sich der zweite Teil der Verarbeitung von Fuzzy-Daten widmet. Die verschiedenen Kapitel sind dabei durch die beiden Themen verbunden. So werden insbesondere auch Methoden für die Komplexitätsreduktion von Fuzzy-Datensätzen entwickelt

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Książki na temat "Fuzzy analysis"

Bandemer, Hans. Fuzzy data analysis. Dordrecht, Netherlands: Kluwer Academic Publishers, 1992.

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Bandemer, Hans, and Wolfgang Näther. Fuzzy Data Analysis. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2506-2.

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1949-, Fedrizzi Mario, and Kacprzyk Janusz, eds. Fuzzy regression analysis. Warsaw: Omnitech Press, 1992.

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1939-, Bezdek James C., International Fuzzy Systems Association, and FIP-84 (1984 :. Kauai), eds. Analysis of fuzzy information. Boca Raton, Fla: CRC Press, 1987.

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1939-, Bezdek James C., ed. Analysis of fuzzy information. Boca Raton, Fla: CRC Press, 1987.

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1939-, Bezdek James C., ed. Analysis of fuzzy information. Boca Raton, Fla: CRC Press, 1987.

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S, Farinwata Shehu, Filev Dimitar P. 1959-, and Langari Reza, eds. Fuzzy control: Synthesis and analysis. Chichester: Wiley, 2000.

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Rabetge, Christian. Fuzzy Sets in der Netzplantechnik. Wiesbaden: Deutscher Universitäts-Verlag, 1991.

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Ishikawa, Akira, and Terry L. Wilson. Analysis and Evaluation of Fuzzy Systems. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0635-1.

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Cho, Yeol Je, Themistocles M. Rassias, and Reza Saadati. Fuzzy Operator Theory in Mathematical Analysis. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93501-0.

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Części książek na temat "Fuzzy analysis"

Bede, Barnabas. "Fuzzy Analysis." In Mathematics of Fuzzy Sets and Fuzzy Logic, 137–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_8.

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Zimmermann, H. J. "Fuzzy Analysis." In Fuzzy Set Theory—and Its Applications, 91–107. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8702-0_7.

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Zimmermann, H. J. "Fuzzy Analysis." In Fuzzy Set Theory—and Its Applications, 93–109. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0646-0_7.

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Zimmermann, H. J. "Fuzzy Analysis." In Fuzzy Set Theory — and Its Applications, 91–108. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7949-0_7.

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Zimmermann, H. J. "Fuzzy Analysis." In Fuzzy Set Theory — and Its Applications, 83–101. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-015-7153-1_7.

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Bandemer, Hans, and Wolfgang NÄther. "Fuzzy data." In Fuzzy Data Analysis, 89–120. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2506-2_4.

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Berthold, Michael. "Fuzzy Logic." In Intelligent Data Analysis, 269–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03969-4_8.

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Bandemer, Hans, and Wolfgang NÄther. "Qualitative analysis." In Fuzzy Data Analysis, 121–83. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2506-2_5.

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Bandemer, Hans, and Wolfgang NÄther. "Quantitative analysis." In Fuzzy Data Analysis, 185–239. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2506-2_6.

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Mordeson, John N., Sunil Mathew, and G. Gayathri. "Nonstandard Analysis." In Fuzzy Graph Theory, 21–52. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23108-7_2.

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Streszczenia konferencji na temat "Fuzzy analysis"

Nakamura, Tsuyoshi, and Takuya Miura. "Analysis of the Acoustic Characteristics of Takarazuka Revue Member Names." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611782.

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Jefferson, Chris, Han Liu, and Mihaela Cocea. "Fuzzy approach for sentiment analysis." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015577.

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Ida, Masaaki. "Textual information and correspondence analysis in curriculum analysis." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277328.

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Massouros, Ch G., and G. G. Massouros. "On 2-element fuzzy and mimic fuzzy hypergroups." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756632.

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She, Wei, and Yangdong Ye. "Fault diagnosis via fuzzy time analysis." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6250796.

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Granville, N. W. "Fuzzy analysis of fuzzy images." In IEE Colloquium Intelligent Methods in Healthcare and Medical Applications. IEE, 1998. http://dx.doi.org/10.1049/ic:19981034.

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Molina-Lozano, Heron, Edgar E. Vallejo-Clemente, and Juan E. Morett-Sanchez. "DNA sequence analysis using fuzzy grammars." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630631.

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Suleman, Abdul. "Validation of archetypal analysis." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015385.

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Yeh, Chi-Tsuen. "A formula for fuzzy linear regression analysis." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007563.

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Yeh, Jen-Wei, Shun-Feng Su, Jin-Tsong Jeng, and Bor-Sen Chen. "On learning analysis of neural fuzzy systems." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584389.

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Raporty organizacyjne na temat "Fuzzy analysis"

Gluckman, Albert G., and Aivars Celmins. Cost Effectiveness Analysis Using Fuzzy Set Theory. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada274003.

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Zwick, Rami, Edward Carlstein, and David Budescu. Measures of Similarity between Fuzzy Concepts: A Comparative Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada189430.

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Cooper, J. A. Fuzzy-algebra uncertainty analysis for abnormal-environment safety assessment. Office of Scientific and Technical Information (OSTI), January 1994. http://dx.doi.org/10.2172/10119580.

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Marques, Paulo. Outsiderness among young people in Europe: A fuzzy set analysis. DINÂMIA'CET-IUL, 2014. http://dx.doi.org/10.7749/dinamiacet-iul.wp.2014.03.

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Robert S. Balch and Ronald F. Broadhead. A Customizable Fuzzy Expert System for Regional and Local Play Analysis. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/926645.

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Xu, Li. Fuzzy multiobjective mathematical programming in economic systems analysis: design and method. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.471.

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Liang, Qilian, and Haining Shu. Wireless Sensor Network Lifetime Analysis Using Interval Type-2 Fuzzy Logic Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada428428.

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.

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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, June 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 to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.

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Chang, Shing I., E. S. Lee, and Steven R. Hanna. A Comparative Study of Multivariate Analysis for Selection and Classification Using Fuzzy Measures and Reasoning. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada397639.

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