Relevant bibliographies by topics / Fuzzy set theory

Academic literature on the topic 'Fuzzy set theory'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Fuzzy set theory"

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Lehmann, Ingo, Richard Weber, and Hans Jürgen Zimmermann. "Fuzzy set theory." Operations-Research-Spektrum 14, no. 1 (March 1992): 1–9. http://dx.doi.org/10.1007/bf01783496.

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Zimmermann, H. J. "Fuzzy set theory." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 3 (April 16, 2010): 317–32. http://dx.doi.org/10.1002/wics.82.

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Barr, Michael. "Fuzzy Set Theory and Topos Theory." Canadian Mathematical Bulletin 29, no. 4 (December 1, 1986): 501–8. http://dx.doi.org/10.4153/cmb-1986-079-9.

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AbstractThe relation between the categories of Fuzzy Sets and that of Sheaves is explored and the precise connection between them is explicated. In particular, it is shown that if the notion of fuzzy sets is further fuzzified by making equality (as well as membership) fuzzy, the resultant categories are indeed toposes.
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Takeuti, Gaisi, and Satoko Titani. "Fuzzy logic and fuzzy set theory." Archive for Mathematical Logic 32, no. 1 (January 1992): 1–32. http://dx.doi.org/10.1007/bf01270392.

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Radu, C., and R. Wilkerson. "Using fuzzy set theory." IEEE Potentials 14, no. 5 (1996): 33–35. http://dx.doi.org/10.1109/45.481510.

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Hajek, P., and Z. Hanikova. "Interpreting lattice-valued set theory in fuzzy set theory." Logic Journal of IGPL 21, no. 1 (July 18, 2012): 77–90. http://dx.doi.org/10.1093/jigpal/jzs023.

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Soni, Manjula. "Fuzzy Set Theory in Sociology." International Journal for Research in Applied Science and Engineering Technology V, no. IX (September 30, 2017): 1148–51. http://dx.doi.org/10.22214/ijraset.2017.9165.

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Maiers, J., and Y. S. Sherif. "Applications of fuzzy set theory." IEEE Transactions on Systems, Man, and Cybernetics SMC-15, no. 1 (January 1985): 175–89. http://dx.doi.org/10.1109/tsmc.1985.6313408.

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Nakamura, K. "Fuzzy set and possibility theory." Proceedings of the IEEE 73, no. 2 (1985): 382. http://dx.doi.org/10.1109/proc.1985.13157.

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Toth, Herbert. "From fuzzy-set theory to fuzzy set-theory: Some critical remarks on existing concepts." Fuzzy Sets and Systems 23, no. 2 (August 1987): 219–37. http://dx.doi.org/10.1016/0165-0114(87)90060-1.

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Dissertations / Theses on the topic "Fuzzy set theory"

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Mao, Hongwei. "Estimating labour productivity using fuzzy set theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0019/MQ47065.pdf.

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Raghfar, Hossein. "Application of fuzzy set theory to poverty analysis." Thesis, University of Essex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343582.

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Saboo, Jai Vardhan. "An investment analysis model using fuzzy set theory." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/50087.

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Traditional methods for evaluating investments in state-of-the-art technology are sometimes found lacking in providing equitable recommendations for project selection. The major cause for this is the inability of these methods to handle adequately uncertainty and imprecision, and account for every aspect of the project, economic and non-economic, tangible and intangible. Fuzzy set theory provides an alternative to probability theory for handling uncertainty, while at the same time being able to handle imprecision. It also provides a means of closing the gap between the human thought process and the computer, by enabling the establishment of linguistic quantifiers to describe intangible attributes. Fuzzy set theory has been used successfully in other fields for aiding the decision-making process. The intention of this research has been the application of fuzzy set theory to aid investment decision making. The research has led to the development of a structured model, based on theoretical algorithms developed by Buckley and others. The model looks at a project from three different standpoints- economic, operational, and strategic. It provides recommendations by means of five different values for the project desirability, and results of two sensitivity analyses. The model is tested on a hypothetical case study. The end result is a model that can be used as a basis for promising future development of investment analysis models.
Master of Science
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Bhattacharyya, Kakali. "Classification of rock masses based on fuzzy set theory." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29490352.

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Murali, V. "A study of universal algebras in fuzzy set theory." Thesis, Rhodes University, 1988. http://hdl.handle.net/10962/d1001983.

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This thesis attempts a synthesis of two important and fast developing branches of mathematics, namely universal algebra and fuzzy set theory. Given an abstract algebra [X,F] where X is a non-empty set and F is a set of finitary operations on X, a fuzzy algebra [I×,F] is constructed by extending operations on X to that on I×, the set of fuzzy subsets of X (I denotes the unit interval), using Zadeh's extension principle. Homomorphisms between fuzzy algebras are defined and discussed. Fuzzy subalgebras of an algebra are defined to be elements of a fuzzy algebra which respect the extended algebra operations under inclusion of fuzzy subsets. The family of fuzzy subalgebras of an algebra is an algebraic closure system in I×. Thus the set of fuzzy subalgebras is a complete lattice. A fuzzy equivalence relation on a set is defined and a partition of such a relation into a class of fuzzy subsets is derived. Using these ideas, fuzzy functions between sets, fuzzy congruence relations, and fuzzy homomorphisms are defined. The kernels of fuzzy homomorphisms are proved to be fuzzy congruence relations, paving the way for the fuzzy isomorphism theorem. Finally, we sketch some ideas on free fuzzy subalgebras and polynomial algebras. In a nutshell, we can say that this thesis treats the central ideas of universal algebras, namely subalgebras, homomorphisms, equivalence and congruence relations, isomorphism theorems and free algebra in the fuzzy set theory setting
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Assi, Jolnar Abdulkarim. "Knightian uncertainty modelling and its impact on option pricing : applications of fuzzy set theory, fuzzy measure theory and fuzzy differential calculus." Thesis, City University London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274460.

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Peters, Barry. "Stable fuzzy logic controllers for uncertain dynamic systems." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/18223.

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Melik, Serhat. "Cash Flow Analysis Of Construction Projects Using Fuzzy Set Theory." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612517/index.pdf.

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Construction industry is a one of the most risky sectors due to high level of uncertainties included in the nature of the construction projects. Although there are many reasons, the deficiency of cash is one of the main factors threatening the success of the construction projects and causing business failures. Therefore, an appropriate cash planning technique is necessary for adequate cost control and efficient cash management while considering the risks and uncertainties of the construction projects. The main objective of this thesis is to develop a realistic, reliable and cost-schedule integrated cash flow modeling technique by using fuzzy set theory for including the uncertainties in project cost and schedule resulting from complex and ambiguous nature of construction works. The linguistic expressions are used for utilizing from human judgment and approximate reasoning ability of users for reflecting their experience into the model to create cash flow scenarios. The uncertain cost and duration estimates gathered from experts are inserted in the model as fuzzy numbers. The model provides the user different net cash flow scenarios with fuzzy formats that are beneficial for foreseeing possible cost and schedule threats to the project during the tender stage. The model is generated in Microsoft Excel 2007 using Visual Basic for applications and the model is applied to a case example.
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Al-Zaidan, Amina S. "Mathematical modeling of marine environment contamination using fuzzy set theory." Thesis, Bangor University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248901.

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Almadani, Firdos Mohammed. "Modelling and analysing vague geographical places using fuzzy set theory." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/37352.

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Vagueness is an essential part of how humans perceive and understand the geographical world they occupy. It has now become of increasing important to acknowledge this situation in geographical databases and analyses in the field of Geographical Information Science (GIScience). This research has tackled the wholly original topic of modelling vague geographical places (objects) based on fuzzy set theory with a view to assessing the implications of routing problem around those vague places. The research has focused on the modelling of vague places, for a number of villages and rural settlements, working with national address databases which have numerous ambiguous characteristics which add challenge to the work. It has demonstrated the way in which fuzzy set theory can be used to derive approximate boundaries for vague spatial extents (fuzzy footprint) form sets of precise addresses, reporting rural settlements, recorded in different databases. It has further explored the implications of applying the Travelling Salesman Problem (TSP) in traditional hard village extents versus the modelled fuzzy extents. The introduced methods evaluate the usefulness of fuzzy set theory in modelling and analysing such vague regions. The results imply that the fuzzy model is more efficient than the traditional hard, crisp model of approximating the spatial extent of rural areas. However, the TSP results showed that longer tours were mostly found in the fuzzy model than the traditional crisp model. This is mainly affected by the scale factor of rural areas, considering the relatively small distances between villages. One challenge for the approach outlined here is to incorporate this method applied in other novel analyses of geographical information based on fuzzy representation of geographical phenomena.
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Books on the topic "Fuzzy set theory"

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Lowen, R. Fuzzy Set Theory. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8741-9.

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Smithson, Michael, and Jay Verkuilen. Fuzzy Set Theory. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2006. http://dx.doi.org/10.4135/9781412984300.

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H, St Clair Ute, and Yuan Bo, eds. Fuzzy set theory: Foundations and applications. Upper Saddle River, NJ: Prentice Hall, 1997.

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Fuzzy set theory and its applications. Boston: Kluwer-Nijhoff Pub., 1985.

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Zimmermann, H. J. Fuzzy set theory--and its applications. 2nd ed. Boston: Kluwer Academic Publishers, 1991.

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Cornelis, Chris, Glad Deschrijver, Mike Nachtegael, Steven Schockaert, and Yun Shi, eds. 35 Years of Fuzzy Set Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16629-7.

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

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

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

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

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Book chapters on the topic "Fuzzy set theory"

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Gottwald, Siegfried. "Basic fuzzy set theory." In Fuzzy Sets and Fuzzy Logic, 37–96. Wiesbaden: Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-86812-1_2.

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Lowen, R. "Elementary Set Theory." In Fuzzy Set Theory, 1–19. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8741-9_1.

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Klement, Erich Peter, Radko Mesiar, and Endre Pap. "Fuzzy set theory." In Trends in Logic, 249–64. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9540-7_12.

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Mordeson, John N., Mark J. Wierman, Terry D. Clark, Alex Pham, and Michael A. Redmond. "Fuzzy Set Theory." In Linear Models in the Mathematics of Uncertainty, 3–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35224-9_1.

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Krejčí, Jana. "Fuzzy Set Theory." In Pairwise Comparison Matrices and their Fuzzy Extension, 57–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77715-3_3.

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Shanahan, James G. "Fuzzy Set Theory." In Soft Computing for Knowledge Discovery, 35–66. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4335-0_3.

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Ebrahimnejad, Ali, and José Luis Verdegay. "Fuzzy Set Theory." In Fuzzy Sets-Based Methods and Techniques for Modern Analytics, 1–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73903-8_1.

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Xu, Jiuping, and Xiaoyang Zhou. "Fuzzy Set Theory." In Fuzzy-Like Multiple Objective Decision Making, 1–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16895-6_1.

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Werro, Nicolas. "Fuzzy Set Theory." In Fuzzy Classification of Online Customers, 7–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_2.

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Lai, Young-Jou, and Ching-Lai Hwang. "Fuzzy Set Theory." In Lecture Notes in Economics and Mathematical Systems, 14–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-48753-8_2.

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Conference papers on the topic "Fuzzy set theory"

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Alkhazaleh, Shawkat. "n-valued refined neutrosophic soft set theory." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7738004.

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"FUZZY SET THEORY BASED STUDENT EVALUATION." In International Conference on Fuzzy Computation. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002312300530058.

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Medynskaya, M. K. "Fuzzy set theory. The concept of fuzzy sets." In 2015 XVIII International Conference on Soft Computing and Measurements (SCM). IEEE, 2015. http://dx.doi.org/10.1109/scm.2015.7190402.

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Szmidt, E., and J. F. Baldwin. "Intuitionistic Fuzzy Set Functions, Mass Assignment Theory, Possibility Theory and Histograms." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1681691.

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Hossein Zadeh, Parisa D., and Marek Z. Reformat. "Feature-based similarity assessment in ontology using fuzzy set theory." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251266.

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Ming-Chun Wang, Zheng-Ou Wang, Ming Zhang, and Peng Yan. "Decision rule extraction method based on rough set theory and fuzzy set theory." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527312.

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Wierman, M. J. "Yet another axiomization of fuzzy set theory." In IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04. IEEE, 2004. http://dx.doi.org/10.1109/nafips.2004.1336289.

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Saeed, Faisel, K. M. George, and Huizhu Lu. "Image contrast enhancement using fuzzy set theory." In the 1992 ACM/SIGAPP Symposium. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/143559.143668.

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Xia, Xintao, Zhongyu Wang, and Yongsheng Gao. "Optimal measurement planning using fuzzy-set theory." In Fifth International Symposium on Instrumentation and Control Technology, edited by Guangjun Zhang, Huijie Zhao, and Zhongyu Wang. SPIE, 2003. http://dx.doi.org/10.1117/12.521921.

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Oh, S. "Distributed spectral estimation using fuzzy set theory." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150150.

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Reports on the topic "Fuzzy set theory"

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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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Esteva, Francesc. On Negations and Algebras in Fuzzy Set Theory. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada604012.

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Goodman, I. R., and V. M. Bier. A Re-Examination of the Relationship between Fuzzy Set Theory and Probability Theory. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada240243.

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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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