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Relevant bibliographies by topics / Fuzzy logic set

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Academic literature on the topic 'Fuzzy logic set'

Author: Grafiati

Published: 2 June 2025

Last updated: 22 June 2025

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Journal articles on the topic "Fuzzy logic set"

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GAO, XIAOYU, Q. S. GAO, Y. HU, and L. LI. "A PROBABILITY-LIKE NEW FUZZY SET THEORY." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 03 (2006): 441–62. http://dx.doi.org/10.1142/s0218001406004697.

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In this paper, the reasons for the shortcoming of Zadeh's fuzzy set theory — it cannot correctly reflect different kinds of fuzzy phenomenon in the natural world — are discussed. In addition, the proof of the error of Zadeh's fuzzy set theory — it incorrectly defined the set complement that cannot exist in Zadeh's fuzzy set theory — is proposed. This error of Zadeh's fuzzy set theory causes confusion in thinking, logic and conception. It causes the seriously mistaken belief that logics of fuzzy sets necessarily go against classical and normal thinking, logic and conception. Two new fuzzy set theories, C-fuzzy set theory and probability-like fuzzy set theory, the N-fuzzy set theory, are proposed. The two are equivalent, and both overcome the error and shortcoming of Zadeh's fuzzy set theory, and they are consistent with normal, natural and classical thinking, logic and concepts. The similarities of N-fuzzy set theory with probability theory are also examined.

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Takeuti, Gaisi, and Satoko Titani. "Fuzzy logic and fuzzy set theory." Archive for Mathematical Logic 32, no. 1 (1992): 1–32. http://dx.doi.org/10.1007/bf01270392.

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BOBILLO, FERNANDO, MIGUEL DELGADO, JUAN GÓMEZ-ROMERO, and UMBERTO STRACCIA. "JOINING GÖDEL AND ZADEH FUZZY LOGICS IN FUZZY DESCRIPTION LOGICS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 04 (2012): 475–508. http://dx.doi.org/10.1142/s0218488512500249.

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Ontologies have succeeded as a knowledge representation formalism in many domains of application. Nevertheless, they are not suitable to represent vague or imprecise information. To overcome this limitation, several extensions to classical ontologies based on fuzzy logic have been proposed. Even though different fuzzy logics lead to fuzzy ontologies with very different logical properties, the combined use of different fuzzy logics has received little attention to date. This paper proposes a fuzzy extension of the Description Logic [Formula: see text] — the logic behind the ontology language OWL 2 — that joins Gödel and Zadeh fuzzy logics. We analyze the properties of the new fuzzy Description Logic in order to provide guidelines to ontology developers to exploit the best features of each fuzzy logic. The proposal also considers degrees of truth belonging to a finite set of linguistic terms rather than numerical values, thus being closer to real experts' reasonings. We prove the decidability of the combined logic by presenting a reasoning preserving procedure to obtain a crisp representation for it. This result is generalized to offer a similar reduction that can be applied when any other finite t -norms, t -conorms, negations or implications are considered in the logic.

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buche, antje, jonas buche, and markus b. siewert. "fuzzy logic or fuzzy application? a response to Stockemer’s ‘fuzzy set or fuzzy logic?" European Political Science 15, no. 3 (2016): 359–78. http://dx.doi.org/10.1057/eps.2015.97.

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C, Swathi, Jenifer Ebienazer, Swathi M, and Suruthipriya S. "Fuzzy Logic." International Journal of Innovative Research in Information Security 09, no. 03 (2023): 147–52. http://dx.doi.org/10.26562/ijiris.2023.v0903.19.

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Fuzzy logic is a mathematical framework for reasoning about ambiguous or inaccurate information. It is founded on the idea that truth can be stated as a degree of membership in a fuzzy set rather than as a binary value of true or untrue. Fuzzy logic is used in control systems, artificial intelligence, and decision-making. This paper defines fuzzy logic and discusses its key concepts, mathematical underpinnings, and applications. We look at the benefits and drawbacks.

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Lano, K. "Intuitionistic modal logic and set theory." Journal of Symbolic Logic 56, no. 2 (1991): 497–516. http://dx.doi.org/10.2307/2274696.

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The mathematical treatment of the concepts of vagueness and approximation is of increasing importance in artificial intelligence and related research. The theory of fuzzy sets was created by Zadeh [Z] to allow representation and mathematical manipulation of situations of partial truth, and proceeding from this a large amount of theoretical and applied development of this concept has occurred. The aim of this paper is to develop a natural logic and set theory that is a candidate for the formalisation of the theory of fuzzy sets. In these theories the underlying logic of properties and sets is intuitionistic, but there is a subset of formulae that are ‘crisp’, classical and two-valued, which represent the certain information. Quantum logic or logics weaker than intuitionistic can also be adopted as the basis, as described in [L]. The relationship of this theory to the intensional set theory MZF of [Gd] and the global intuitionistic set theory GIZF of Takeuti and Titani [TT] is also treated.

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Imran, Hassan, and Kar Suman. "The application of fuzzy logic techniques to improve decision making in apparel size." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 607–15. https://doi.org/10.5281/zenodo.10842475.

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Traditional set theory, or crisp set theory, is built on the concept of crisp sets. These are sets for which the membership of an element within a set is defined as either true or false; in or out; 1 or 0. This construction is extremely useful, as mathematics has shown, but it struggles to model concepts of our world that possess vagueness or uncertainty. Therefore, we explore an expansion of set theory to allow an element to be partially within a set, thus constituting what is known as a fuzzy set. This paper introduces the basic concept of fuzzy sets, which includes fuzzy sets and crisp sets, as well as the operations of a fuzzy set and fuzzy classification systems. Fuzzy logic has been utilized to solve numerous textile-related difficulties, one of which was determining the proper clothing size. In this study, we examined fuzzy logic applications in textiles, such as the construction of fuzzy expert systems and fuzzy logic for predicting clothing size. This research demonstrates that when determining the correct size of clothing, the outcome is heavily reliant on fuzzy logic.

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Gehrke, Mai, Carol Walker, and Elbert Walker. "A Mathematical Setting for Fuzzy Logics." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 03 (1997): 223–38. http://dx.doi.org/10.1142/s021848859700021x.

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The setup of a mathematical propositional logic is given in algebraic terms, describing exactly when two choices of truth value algebras give the same logic. The propositional logic obtained when the algebra of truth values is the real numbers in the unit interval equipped with minimum, maximum and -x=1-x for conjunction, disjunction and negation, respectively, is the standard propositional fuzzy logic. This is shown to be the same as three-valued logic. The propositional logic obtained when the algebra of truth values is the set {(a, b)|a≤ b and a,b∈[0,1]} of subintervals of the unit interval with component-wise operations, is propositional interval-valued fuzzy logic. This is shown to be the same as the logic given by a certain four element lattice of truth values. Since both of these logics are equivalent to ones given by finite algebras, it follows that there are finite algorithms for determining when two statements are logically equivalent within either of these logics. On this topic, normal forms are discussed for both of these logics.

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Zhang, Jinjin, Xiaoxia Zhou, Yan Zhang, and Lixing Tan. "Fuzzy Epistemic Logic: Fuzzy Logic of Doxastic Attitudes." Mathematics 13, no. 7 (2025): 1105. https://doi.org/10.3390/math13071105.

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In traditional epistemic logic—particularly modal logic—agents are often assumed to have complete and certain knowledge, which is unrealistic in real-world scenarios where uncertainty, imprecision, and the incompleteness of information are common. This study proposes an extension of the logic of doxastic attitudes to a fuzzy setting, representing beliefs or knowledge as continuous values in the interval [0, 1] rather than binary Boolean values. This approach offers a more nuanced and realistic modeling of belief states, capturing the inherent uncertainty and vagueness in human reasoning. We introduce a set of axioms for the fuzzy logic of doxastic attitudes, formalizing how agents reason with regard to uncertain beliefs. The theoretical foundations of this logic are established through proofs of soundness and completeness. To demonstrate practical utility, we present a concrete example, illustrating how the fuzzy logic of doxastic attitudes can model uncertain preferences and beliefs.

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Whalen, Thomas, and Brian Schott. "Usuality, regularity, and fuzzy set logic." International Journal of Approximate Reasoning 6, no. 4 (1992): 481–504. http://dx.doi.org/10.1016/0888-613x(92)90001-g.

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Dissertations / Theses on the topic "Fuzzy logic set"

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Ornelas, Gilbert. "Set-valued extensions of fuzzy logic classification theorems /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Patel, Chintan. "Evaluating Trench Safety Using Fuzzy Logic Concept and Fuzzy Set Models." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1419353000.

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Hornik, Kurt, and David Meyer. "Generalized and Customizable Sets in R." American Statistical Association, 2009. http://epub.wu.ac.at/4002/1/sets.pdf.

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We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy (multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (authors' abstract)

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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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Hill, Carla. "Mass assignments for inductive logic programming." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325748.

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Greenfield, Sarah. "Type-2 fuzzy logic : circumventing the defuzzification bottleneck." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/7088.

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Type-2 fuzzy inferencing for generalised, discretised type-2 fuzzy sets has been impeded by the computational complexity of the defuzzification stage of the fuzzy inferencing system. Indeed this stage is so complex computationally that it has come to be known as the defuzzification bottleneck. The computational complexity derives from the enormous number of embedded sets that have to be individually processed in order to effect defuzzification. Two new approaches to type-2 defuzzification are presented, the sampling method and the Greenfield-Chiclana Collapsing Defuzzifier. The sampling method and its variant, elite sampling, are techniques for the defuzzification of generalised type-2 fuzzy sets. In these methods a relatively small sample of the totality of embedded sets is randomly selected and processed. The small sample size drastically reduces the computational complexity of the defuzzification process, so that it may be speedily accomplished. The Greenfield-Chiclana Collapsing Defuzzifier relies upon the concept of the representative embedded set, which is an embedded set having the same defuzzified value as the type-2 fuzzy set that is to be defuzzified. By a process termed collapsing the type-2 fuzzy set is converted into a type-1 fuzzy set which, as an approximation to the representative embedded set, is known as the representative embedded set approximation. This type-1 fuzzy set is easily defuzzified to give the defuzzified value of the original type-2 fuzzy set. By this method the computational complexity of type-2 defuzzification is reduced enormously, since the representative embedded set approximation replaces the entire collection of embedded sets. The strategy was conceived as a generalised method, but so far only the interval version has been derived mathematically. The grid method of discretisation for type-2 fuzzy sets is also introduced in this thesis. Work on the defuzzification of type-2 fuzzy sets began around the turn of the millennium. Since that time a number of investigators have contributed methods in this area. These different approaches are surveyed, and the major methods implemented in code prior to their experimental evaluation. In these comparative experiments the grid method of defuzzification is employed. The experimental results show beyond doubt that the collapsing method performs the best of the interval alternatives. However, though the sampling method performs well experimentally, the results do not demonstrate it to be the best performing generalised technique.

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Meyer, David, and Kurt Hornik. "Generalized and Customizable Sets in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/1062/1/document.pdf.

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We present data structures and algorithms for sets and some generalizations thereof (fuzzy sets, multisets, and fuzzy multisets) available for R through the sets package. Fuzzy (multi-)sets are based on dynamically bound fuzzy logic families. Further extensions include user-definable iterators and matching functions. (author´s abstract)<br>Series: Research Report Series / Department of Statistics and Mathematics

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Vicenová, Ivana. "Aplikace fuzzy logiky při hodnocení dodavatelů firmy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2015. http://www.nusl.cz/ntk/nusl-224855.

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This diploma thesis deals with the design of fuzzy model for decision among suppliers of the metallurgical material for the firm VÚEZ, a.s. Levice. It describes methods and processes of a model creating. The goal is to make a decision-making system that helps a firm to make decisions among suppliers more effective that will be usable in real world and is based on firm’s requests.

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Ralbovský, Martin. "Fuzzy GUHA." Doctoral thesis, Vysoká škola ekonomická v Praze, 2006. http://www.nusl.cz/ntk/nusl-77047.

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The GUHA method is one of the oldest methods of exploratory data analysis, which is regarded as part of the data mining or knowledge discovery in databases (KDD) scienti_c area. Unlike many other methods of data mining, the GUHA method has firm theoretical foundations in logic and statistics. In scope of the method, finding interesting knowledge corresponds to finding special formulas in satisfactory rich logical calculus, which is called observational calculus. The main topic of the thesis is application of the "fuzzy paradigm" to the GUHA method By the term "fuzzy paradigm" we mean approaches that use many-valued membership degrees or truth values, namely fuzzy set theory and fuzzy logic. The thesis does not aim to cover all the aspects of this application, it emphasises mainly on: - Association rules as the most prevalent type of formulas mined by the GUHA method - Usage of fuzzy data - Logical aspects of fuzzy association rules mining - Comparison of the GUHA theory to the mainstream fuzzy association rules - Implementation of the theory using the bit string approach The thesis throughoutly elaborates the theory of fuzzy association rules, both using the theoretical apparatus of fuzzy set theory and fuzzy logic. Fuzzy set theory is used mainly to compare the GUHA method to existing mainstream approaches to formalize fuzzy association rules, which were studied in detail. Fuzzy logic is used to define novel class of logical calculi called logical calculi of fuzzy association rules (LCFAR) for logical representation of fuzzy association rules. The problem of existence of deduction rules in LCFAR is dealt in depth. Suitable part of the proposed theory is implemented in the Ferda system using the bit string approach. In the approach, characteristics of examined objects are represented as strings of bits, which in the crisp case enables efficient computation. In order to maintain this feature also in the fuzzy case, a profound low level testing of data structures and algoritms for fuzzy bit strings have been carried out as a part of the thesis.

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Pergl, Miroslav. "Vývojové prostředí pro umělou inteligenci Modul fuzzy čísel." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218054.

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Master’s thesis deals with mathematical operation with fuzzy numbers. The first part of the thesis deals with theoretical knowledge of fuzzy arithmetic and defines fuzzy sets, fuzzy numbers, universum and five membership function used in program. In the concrete it describes – cut method for dealing with fuzzy numbers as with limited interval for specific level which simplifies computation. The second part of the thesis contains description of programmed module for mathematical operation with fuzzy numbers. There is described creation of user interface which is using to set parameters of computation. There are also described support functions which make operation with fuzzy numbers possible and operation ensures output.

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Books on the topic "Fuzzy logic set"

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Novák, Vilém. Mathematical principles of fuzzy logic. Kluwer Academic, 1999.

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Nguyen, Hung T. A first course in fuzzy logic. 2nd ed. Chapman & Hall, 2000.

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Zimmermann, H. J. Fuzzy Set Theory - and Its Applications. Springer Netherlands, 1991.

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Ruan, Da. Fuzzy Set Theory and Advanced Mathematical Applications. Springer US, 1995.

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Reghiș, Mircea. Classical and fuzzy concepts in mathematical logic and applications. CRC Press, 1998.

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Janssen, Jeroen, Steven Schockaert, Dirk Vermeir, and Martine de Cock. Answer Set Programming for Continuous Domains: A Fuzzy Logic Approach. Atlantis Press, 2012. http://dx.doi.org/10.2991/978-94-91216-59-6.

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Lowen, R. Fuzzy Set Theory: Basic Concepts, Techniques and Bibliography. Springer Netherlands, 1996.

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Ulrich, Höhle, Klement E. P, and Linz Seminar on Fuzzy Set Theory (14th : 1992 :, eds. Non-classical logics and their applications to fuzzy subsets: A handbook of the mathematical foundations of fuzzy set theory. Kluwr Academic Publishers, 1995.

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author, Malik D. S., and Clark Terry D. author, eds. Application of fuzzy logic to social choice theory. CRC Press, Taylor & Francis Group, 2015.

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I, Baturone, ed. Microelectronic design of fuzzy logic-based systems. CRC, 2000.

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Book chapters on the topic "Fuzzy logic set"

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Lowen, R. "Fuzzy Logic." In Fuzzy Set Theory. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8741-9_6.

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Weik, Martin H. "fuzzy-set logic." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_7848.

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

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

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Gottwald, Siegfried. "Set equations with fuzzy sets." In Fuzzy Sets and Fuzzy Logic. Vieweg+Teubner Verlag, 1993. http://dx.doi.org/10.1007/978-3-322-86812-1_3.

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Kruse, Rudolf, Jörg Gebhardt, and Frank Klawonn. "Numerical and Logical Approaches to Fuzzy Set Theory by the Context Model." In Fuzzy Logic. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_34.

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Janssen, Jeroen, Stijn Heymans, Dirk Vermeir, and Martine De Cock. "Compiling Fuzzy Answer Set Programs to Fuzzy Propositional Theories." In Logic Programming. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89982-2_34.

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Bede, Barnabas. "Fuzzy Set-Theoretic Operations." In Mathematics of Fuzzy Sets and Fuzzy Logic. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_2.

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Janssen, Jeroen, Steven Schockaert, Dirk Vermeir, and Martine De Cock. "General Fuzzy Answer Set Programs." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02282-1_44.

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Bede, Barnabas. "Extensions of Fuzzy Set Theory." In Mathematics of Fuzzy Sets and Fuzzy Logic. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_10.

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Conference papers on the topic "Fuzzy logic set"

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Riera, Juan Vicente, and Joan Torrens. "Fuzzy implications defined on the set of discrete fuzzy numbers." In 7th conference of the European Society for Fuzzy Logic and Technology. Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.97.

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Ornelas, Gilbert, and Vladik Kreinovich. "Set-Valued Extensions of Fuzzy Logic: Classification Theorems." In NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2007. http://dx.doi.org/10.1109/nafips.2007.383899.

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Uhliarik, Ivor. "Solving Fuzzy Answer Set Programs in Product Logic." In 9th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006518303670372.

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Ye, XaoLing, Ling Ling Fu, and anf Yingchao Zhang. "Type-2 Fuzzy Logic System and Level Set." In Third International Conference on Semantics, Knowledge and Grid (SKG 2007). IEEE, 2007. http://dx.doi.org/10.1109/skg.2007.286.

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Ye, XaoLing, Ling Ling Fu, and anf Yingchao Zhang. "Type-2 Fuzzy Logic System and Level Set." In Third International Conference on Semantics, Knowledge and Grid (SKG 2007). IEEE, 2007. http://dx.doi.org/10.1109/skg.2007.62.

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Horton, Michael J., and Richard A. Jones. "Fuzzy logic extended rule set for multitarget tracking." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Michael K. Masten and Larry A. Stockum. SPIE, 1995. http://dx.doi.org/10.1117/12.210422.

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Achiche, Sofiane, and Saeema Ahmed. "Mapping Shape Geometry and Emotions Using Fuzzy Logic." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49290.

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An important aspect of artifact/product design is defining the aesthetic and emotional value. The success of a product is not only dependent on it’s functionality but also on the emotional value that it creates to its user. However, if several designers are faced with a task to create an object that would evoke a certain emotion (aggressive, soft, heavy, friendly, etc.) each would most likely interpret the emotion with a different set of geometric features and shapes. In this paper the authors propose an approach to formalize the relationship between geometric information of a 3D object and the intended emotion using fuzzy logic. To achieve this; 3D objects (shapes) created by design engineering students to match a set of words/emotions were analyzed. The authors identified geometric information as inputs of the fuzzy model and developed a set of fuzzy if/then rules to map the relationships between the fuzzy sets on each input premise and the output premise. In our case the output premise of the fuzzy logic model is the level of belonging to the design context (emotion). An evaluation of how users perceived the shapes was conducted to validate the fuzzy logic model and showed a high correlation between the fuzzy logic model and user perception.

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Wang, Rifeng, Peihe Tang, Chungui Li, and Hao Liu. "Tolerance Rough Set-Inductive Logic Programming (RS-ILP)." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.618.

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Greenfield, Sarah, and Francisco Chiclana. "The Structure of the Type-Reduced Set of a Continuous Type-2 Fuzzy Set." In The 8th conference of the European Society for Fuzzy Logic and Technology. Atlantis Press, 2013. http://dx.doi.org/10.2991/eusflat.2013.102.

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Ivanets, Sergey, Artem Fesenko, and Oleksandr Fesiuk. "FUZZY LOGIC CUSTOM INSTRUCTION SET FOR NIOS II PROCESSOR." In MC&FPGA-2020. 2020. http://dx.doi.org/10.35598/mcfpga.2020.011.

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Reports on the topic "Fuzzy logic set"

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Huang, Z., J. Shimeld, and M. Williamson. Application of computer neural network, and fuzzy set logic to petroleum geology, offshore eastern Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/194121.

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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.

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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 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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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other tasks, however, computers perform very poorly at tasks that humans find to be trivial. A discussion of the differences in the way humans and computer process information is crucial to an understanding of the field of artificial intelligence. In this project, several artificial intelligence/ intelligent control systems were examined: heuristic search techniques, adaptive control, expert systems, fuzzy logic, neural networks, and genetic algorithms. Of these, neural networks showed the most potential for use on large bore engines because of their ability to recognize patterns in incomplete, noisy data. Two sets of experimental tests were conducted to test the predictive capabilities of neural networks. The first involved predicting the ignition timing from combustion pressure histories; the best networks responded within a specified tolerance level 90% to 98.8% of the time. In the second experiment, neural networks were used to predict NOx, A/F ratio, and fuel consumption. NOx prediction accuracy was 91.4%, A/F ratio accuracy was 82.9%, and fuel consumption accuracy was 52.9%. This report documents the assessment of the state of the art of artificial intelligence for application to the monitoring and control of large-bore natural gas engines.

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