Relevant bibliographies by topics / Fuzzy logic. Fuzzy sets. Fuzzy systems

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

Academic literature on the topic 'Fuzzy logic. Fuzzy sets. Fuzzy systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Fuzzy logic. Fuzzy sets. Fuzzy systems"

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Chen,, Guanrong, Trung Tat Pham,, and NM Boustany,. "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems." Applied Mechanics Reviews 54, no. 6 (2001): B102—B103. http://dx.doi.org/10.1115/1.1421114.

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NOVÁK, VILÉM. "FUZZY LOGIC, FUZZY SETS, AND NATURAL LANGUAGES." International Journal of General Systems 20, no. 1 (1991): 83–97. http://dx.doi.org/10.1080/03081079108945017.

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PAPADOPOULOS, BASIL K., and APOSTOLOS SYROPOULOS. "FUZZY SETS AND FUZZY RELATIONAL STRUCTURES AS CHU SPACES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 04 (2000): 471–79. http://dx.doi.org/10.1142/s0218488500000319.

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Chu spaces, which derive from the Chu construct of *-autonomous categories, can be used to represent most mathematical structures. Moreover, the logic of Chu spaces is linear logic. Most efforts to incorporate fuzzy set theory into the realm of linear logic are based on the assumption that fuzzy and linear negation are identical operations. We propose an incorporation based on the opposite assumption and we provide an interpretation of some linear connectives. Furthermore, we show that it is possible to represent any fuzzy relational structure as a Chu space by means of the functor G.
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Azar, Ahmad Taher. "Overview of Type-2 Fuzzy Logic Systems." International Journal of Fuzzy System Applications 2, no. 4 (2012): 1–28. http://dx.doi.org/10.4018/ijfsa.2012100101.

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Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead
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Sabahi, Farnaz, and Mohammad Reza Akbarzadeh-T. "Extended Fuzzy Logic: Sets and Systems." IEEE Transactions on Fuzzy Systems 24, no. 3 (2016): 530–43. http://dx.doi.org/10.1109/tfuzz.2015.2453994.

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HOMENDA, WLADYSLAW, and WITOLD PEDRYCZ. "BALANCED FUZZY COMPUTING UNIT." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 02 (2005): 117–38. http://dx.doi.org/10.1142/s0218488505003357.

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We introduce and study a new concept of fuzzy computing units. This construct is is aimed at coping with "negative" (inhibitory) information and accommodating it in the language of fuzzy sets. The essential concept developed in this study deals with computing units exploiting the concept of balanced fuzzy sets. We recall how the membership notion of fuzzy sets can be extended to the [-1,1] range giving rise to balanced fuzzy sets and then summarize properties of augmented (extended) logic operations for these constructs. We show that this idea is particularly appealing in neurocomputing as the
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Guo, Lankun, Guo-Qiang Zhang, and Qingguo Li. "Fuzzy closure systems onL-ordered sets." Mathematical Logic Quarterly 57, no. 3 (2011): 281–91. http://dx.doi.org/10.1002/malq.201010007.

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Niewiadomski, A., and M. Kacprowicz. "Higher order fuzzy logic in controlling selective catalytic reduction systems." Bulletin of the Polish Academy of Sciences Technical Sciences 62, no. 4 (2014): 743–50. http://dx.doi.org/10.2478/bpasts-2014-0080.

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Abstract This paper presents research on applications of fuzzy logic and higher-order fuzzy logic systems to control filters reducing air pollution [1]. The filters use Selective Catalytic Reduction (SCR) method and, as for now, this process is controlled manually by a human expert. The goal of the research is to control an SCR system responsible for emission of nitrogen oxide (NO) and nitrogen dioxide (NO2) to the air, using SCR with ammonia (NH3). There are two higher-order fuzzy logic systems presented, applying interval-valued fuzzy sets and type-2 fuzzy sets, respectively. Fuzzy sets and
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Tamir, Dan E., and Abraham Kandel. "Axiomatic Theory of Complex Fuzzy Logic and Complex Fuzzy Classes." International Journal of Computers Communications & Control 6, no. 3 (2011): 562. http://dx.doi.org/10.15837/ijccc.2011.3.2135.

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Complex fuzzy sets, classes, and logic have an important role in applications, such as prediction of periodic events and advanced control systems, where several fuzzy variables interact with each other in a multifaceted way that cannot be represented effectively via simple fuzzy operations such as union, intersection, complement, negation, conjunction and disjunction. The initial formulation of these terms stems from the definition of complex fuzzy grade of membership. The problem, however, with these definitions are twofold: 1) the complex fuzzy membership is limited to polar representation w
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Chang, Te-Chuan, C. William Ibbs, and Keith C. Crandall. "A fuzzy logic system for expert systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2, no. 3 (1988): 183–93. http://dx.doi.org/10.1017/s0890060400000640.

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Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge b
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Dissertations / Theses on the topic "Fuzzy logic. Fuzzy sets. Fuzzy systems"

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Houchin, Scott J. "Pendulum : controlling an inverted pendulum using fuzzy logic /." Online version of thesis, 1991. http://hdl.handle.net/1850/11294.

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Talwanga, Matiki. "The principle of inclusion-exclusion and möbius function as counting techniques in finite fuzzy subsets." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005227.

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The broad goal in this thesis is to enumerate elements and fuzzy subsets of a finite set enjoying some useful properties through the well-known counting technique of the principle of inclusion-exclusion. We consider the set of membership values to be finite and uniformly spaced in the real unit interval. Further we define an equivalence relation with regards to the cardinalities of fuzzy subsets providing the Möbius function and Möbius inversion in that context.
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Šeda, Martin. "Aplikace fuzzy logiky při hodnocení dodavatelů firmy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-223948.

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Master's thesis deals with the evaluation of suppliers of selected company using fuzzy logic. Designed fuzzy system allows firm to evaluate individual offers and serves as a support for decision-making.
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唐靜敏 and Ching-mun Tong. "Evolutionary design of fuzzy-logic controllers with minimal rule sets for manufacturing systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243678.

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Tong, Ching-mun. "Evolutionary design of fuzzy-logic controllers with minimal rule sets for manufacturing systems /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25100130.

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Le, Xuqing. "Fire Detection Robot using Type-2 Fuzzy Logic Sensor Fusion." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32471.

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In this research work, an approach for fire detection and estimation robots is presented. The approach is based on type-2 fuzzy logic system that utilizes measured temperature and light intensity to detect fires of various intensities at different distances. Type-2 fuzzy logic system (T2 FLS) is known for not needing exact mathematic model and for its capability to handle more complicated uncertain situations compared with Type-1 fuzzy logic system (T1 FLS). Due to lack of expertise for new facilities, a new approach for training experts’ expertise and setting up T2 FLS parameters from pure da
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Gu, Ling. "Web Shopping Expert Systems Using New Interval Type-2 Fuzzy Reasoning." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_theses/15.

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Finding a product with high quality and reasonable price online is a difficult task due to the fuzzy nature of data and queries. In order to handle the fuzzy problem, a new type-2 fuzzy reasoning based decision support system, the Web Shopping Expert for online users is proposed. In the Web Shopping Expert, an interval type-2 fuzzy logic system is used and a fuzzy output can be obtained using the up-low limit technique, which offers an opportunity to directly employ all the rules and methods of the type-1 fuzzy sets onto the type-2 fuzzy sets. To achieve the best performance the fuzzy inferenc
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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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Hu, Yanting. "Advanced control system for stand-alone diesel engine driven-permanent magnet generator sets." Thesis, De Montfort University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366632.

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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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Books on the topic "Fuzzy logic. Fuzzy sets. Fuzzy systems"

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1932-, Klir George J., and Yuan Bo, eds. Fuzzy sets, fuzzy logic, and fuzzy systems: Selected papers. World Scientific, 1996.

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Tat, Pham Trung, ed. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, 2001.

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G, Chen. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, 2000.

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Fuzzy relational systems: Foundations and principles. Kluwer Academic/Plenum Publishers, 2002.

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Fuzzy logic for beginners. World Scientific, 2001.

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Zadeh, Lotfi Asker. Fuzzy sets and fuzzy information granulation theory: Key selected papers by. Beijing Normal University Press, 2000.

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

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Views on fuzzy sets and systems from different perspectives: Philosophy and logic, criticisms and applications. Springer, 2009.

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Weiling, Chiang, and Lee Jonathan 1960-, eds. Fuzzy logic for the applications to complex systems: Proceedings of the International Joint Conference of CFSA/IFIS/SOFT '95 on Fuzzy Theory and Applications : Taipei, Taiwan, 7-9 December 1995. World Scientific, 1995.

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Austrian Artificial Intelligence Conference (8th 1993 Linz, Austria). Fuzzy logic in artificial intelligence: 8th Austrian Artificial Intelligence Conference, FLAI '93, Linz, Austria, June 28-30, 1993 : proceedings. Springer-Verlag, 1993.

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Book chapters on the topic "Fuzzy logic. Fuzzy sets. Fuzzy systems"

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Castillo, Oscar, and Krassimir Atanassov. "Comments on Fuzzy Sets, Interval Type-2 Fuzzy Sets, General Type-2 Fuzzy Sets and Intuitionistic Fuzzy Sets." In Recent Advances in Intuitionistic Fuzzy Logic Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02155-9_3.

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Mendel, Jerry M. "Type-1 Fuzzy Sets and Fuzzy Logic." In Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51370-6_2.

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Celikyilmaz, Asli, and I. Burhan Türksen. "Fuzzy Sets and Systems." In Modeling Uncertainty with Fuzzy Logic. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89924-2_2.

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Bede, Barnabas. "Single Input Single Output Fuzzy Systems." In Mathematics of Fuzzy Sets and Fuzzy Logic. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_7.

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Bede, Barnabas. "Artificial Neural Networks and Neuro-Fuzzy Systems." In Mathematics of Fuzzy Sets and Fuzzy Logic. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_14.

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Buckley, James J., and Esfandiar Eslami. "Systems of Fuzzy Linear Equations." In An Introduction to Fuzzy Logic and Fuzzy Sets. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1799-7_11.

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Angelov, Plamen. "How Best to Design Fuzzy Sets and Systems." In Fuzzy Logic and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12544-8_19.

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Starczewski, Janusz T. "Algebraic Operations on Fuzzy Valued Fuzzy Sets." In Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29520-1_2.

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Montero, Javier. "Fuzzy Logic and Science." In Views on Fuzzy Sets and Systems from Different Perspectives. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93802-6_3.

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Starczewski, Janusz T. "Uncertainty in Fuzzy Sets." In Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29520-1_1.

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Conference papers on the topic "Fuzzy logic. Fuzzy sets. Fuzzy systems"

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Mendel, Jerry M. "Human-Inspired—Zadeh—Sets and Logic." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177564.

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Yeganejou, Mojtaba, and Scott Dick. "Inductive learning of classifiers via complex fuzzy sets and logic." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015687.

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Cuong, Bui Cong, and Van Hai Pham. "Some Fuzzy Logic Operators for Picture Fuzzy Sets." In 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2015. http://dx.doi.org/10.1109/kse.2015.20.

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Venetis, Tassos, Giorgos Stoilos, Giorgos Stamou, and Stefanos Kollias. "f-DLPs: Extending Description Logic Programs with Fuzzy Sets and Fuzzy Logic." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295599.

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Ehtiawesh, M., and M. Mahfouf. "Interval type-2 fuzzy sets for Self-Organising Fuzzy Logic based control with on-line PSO Optimisation." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337885.

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Pekaslan, Direnc, Christian Wagner, and Jonathan M. Garibaldi. "Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858800.

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Edwards, Dean B., and John R. Canning. "An Algorithm for Designing Conventional and Fuzzy Logic Control Systems." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/cie-4465.

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Abstract This paper presents an algorithm that can be used to design either conventional or fuzzy logic control systems. In order to use the algorithm, the engineer must first choose a performance index for the system which he or she wants to optimize relative to some specified design parameters. For conventional state space controllers, the design parameters are the feedback constants associated with the state variables of the system. For fuzzy logic controllers, the design parameters are the parameters used to define the fuzzy sets for the input state and control variables. We use the algori
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Garibaldi, Jonathan M., and Sergio Guadarrama. "Constrained type-2 fuzzy sets." In 2011 IEEE Symposium On Advances In Type-2 Fuzzy Logic Systems - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/t2fuzz.2011.5949562.

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Coupland, Simon, and Stephen G. Matthews. "Using nonstationary fuzzy sets to improve the tractability of fuzzy association rules." In 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ). IEEE, 2013. http://dx.doi.org/10.1109/t2fzz.2013.6613293.

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Barbosa, Rafael, and Sandro Ferreira. "Industrial Gas Turbine Diagnostics Using Fuzzy Logic." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68767.

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In this work a fuzzy based fault diagnosis system for an industrial gas turbine engine is presented. The system compares measured parameters and those calculated by a computer model, which sets the new and clean reference of the equipment. A fuzzy system classifies and quantifies the faults, which includes part load operation and takes into consideration the compressor variable geometry, which implies in a modification of the deviation pattern between the reference and the model. A commercial software, the GSP (Gas turbine Simulation Program), has been used for the process simulation. The refe
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Reports on the topic "Fuzzy logic. Fuzzy sets. Fuzzy systems"

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

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