Relevant bibliographies by topics / Fuzzy Rule-based Systems

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  2. Dissertations / Theses
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Academic literature on the topic 'Fuzzy Rule-based Systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Fuzzy Rule-based Systems"

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Lotfi, A., and M. Howarth. "Noninteractive fuzzy rule-based systems." Information Sciences 99, no. 3-4 (1997): 219–34. http://dx.doi.org/10.1016/s0020-0255(96)00271-x.

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Su, Pan, and Xueying Ren. "Fuzzy Rule Interpolation Methods Based on Sparse Rule Bases." International Journal of Computer Science and Information Technology 5, no. 3 (2025): 83–91. https://doi.org/10.62051/ijcsit.v5n3.08.

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Fuzzy rule interpolation algorithms have broad applications in computational fuzzy inference systems. This paper systematically introduces interpolation methods based on α-cuts. It focuses on two classical α-cut-based interpolation methods: the KH fuzzy rule interpolation method and the Lagrange fuzzy rule interpolation method. Through theoretical analysis and comparative studies, the fundamental principles, performance characteristics, and limitations of these two interpolation algorithms are explored in depth. Based on this, a fuzzy inference system for the "tip calculation problem" was cons
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Nozaki, K., H. Ishibuchi, and H. Tanaka. "Adaptive fuzzy rule-based classification systems." IEEE Transactions on Fuzzy Systems 4, no. 3 (1996): 238–50. http://dx.doi.org/10.1109/91.531768.

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Ishibuchi, H., and T. Yamamoto. "Rule weight specification in fuzzy rule-based classification systems." IEEE Transactions on Fuzzy Systems 13, no. 4 (2005): 428–35. http://dx.doi.org/10.1109/tfuzz.2004.841738.

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Starczewski, Janusz T., Piotr Goetzen, and Christian Napoli. "Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems." Journal of Artificial Intelligence and Soft Computing Research 10, no. 4 (2020): 271–85. http://dx.doi.org/10.2478/jaiscr-2020-0018.

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AbstractIn real-world approximation problems, precise input data are economically expensive. Therefore, fuzzy methods devoted to uncertain data are in the focus of current research. Consequently, a method based on fuzzy-rough sets for fuzzification of inputs in a rule-based fuzzy system is discussed in this paper. A triangular membership function is applied to describe the nature of imprecision in data. Firstly, triangular fuzzy partitions are introduced to approximate common antecedent fuzzy rule sets. As a consequence of the proposed method, we obtain a structure of a general (non-interval)
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Ravi, Chandrasekar, and Neelu Khare. "Review of Fuzzy Rule Based Classification systems." Research Journal of Pharmacy and Technology 9, no. 8 (2016): 1299. http://dx.doi.org/10.5958/0974-360x.2016.00247.x.

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Babuska, R., A. Gegov, and H. B. Verbruggen. "Decoupling of Multivariable Rule-Based Fuzzy Systems." IFAC Proceedings Volumes 31, no. 29 (1998): 11. http://dx.doi.org/10.1016/s1474-6670(17)38319-2.

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Babuška, R., A. Gegov, and H. B. Verbruggen. "Decoupling of Multivariable Rule-Based Fuzzy Systems." IFAC Proceedings Volumes 31, no. 29 (1998): 13–16. http://dx.doi.org/10.1016/s1474-6670(17)38913-9.

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Henzgen, Sascha, Marc Strickert, and Eyke Hüllermeier. "Visualization of evolving fuzzy rule-based systems." Evolving Systems 5, no. 3 (2014): 175–91. http://dx.doi.org/10.1007/s12530-014-9110-4.

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Lotfi, A., H. C. Andersen, and A. C. Tsoi. "Matrix formulation of fuzzy rule-based systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, no. 2 (1996): 332–40. http://dx.doi.org/10.1109/3477.485885.

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Dissertations / Theses on the topic "Fuzzy Rule-based Systems"

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Baaj, Ismaïl. "Explainability of possibilistic and fuzzy rule-based systems." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS021.

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Aujourd'hui, les progrès de l'Intelligence Artificielle (IA) ont conduit à l'émergence de systèmes capables d'automatiser des processus complexes, en utilisant des modèles qui peuvent être difficiles à comprendre par les humains. Lorsque les humains utilisent ces systèmes d'IA, il est bien connu qu'ils veulent comprendre leurs comportements et leurs actions, car ils ont davantage confiance dans les systèmes qui peuvent expliquer leurs choix, leurs hypothèses et leurs raisonnements. La capacité d'explication des systèmes d'IA est devenue une exigence des utilisateurs, en particulier dans les en
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Cruz, Anderson Paiva. "On Fuzzy Implication Classes - Towards Extensions of Fuzzy Rule-Based Systems." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18684.

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Made available in DSpace on 2015-03-03T15:47:46Z (GMT). No. of bitstreams: 1 AndersonPC_DISSERT.pdf: 1402040 bytes, checksum: 960b15bc1392a94fb7ba8ba980e3a0b4 (MD5) Previous issue date: 2012-12-20<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior<br>Atualmente, h? diferentes defini??es de implica??es fuzzy aceitas na literatura. Do ponto de vista te?rico, esta falta de consenso demonstra que h? discord?ncias sobre o real significado de "implica??o l?gica" nos contextos Booleano e fuzzy. Do ponto de vista pr?tico, isso gera d?vidas a respeito de quais "operadores de implica
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Carse, Brian. "Artificial evolution of fuzzy and temporal rule based systems." Thesis, University of the West of England, Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267551.

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Karmakar, Gour Chandra 1970. "An integrated fuzzy rule-based image segmentation framework." Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8752.

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Moon, Myung Soo. "Rule-Based Approaches for Controlling on Mode Dynamic Systems." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30684.

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This dissertation presents new fuzzy logic techniques for designing control systems for a wide class of complex systems. The methods are developed in detail for a crane system which contains one rigid-body and one oscillation mode. The crane problem is to transfer the rigid body a given distance such that the pendulation of the oscillation mode is regulated at the final time using a single control input. The investigations include in-depth studies of the time-optimal crane control problem as an integral part of the work. The main contributions of this study are: (1) Development of rule-based
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Vetcha, Sarat Babu. "Fault diagnosis in pumps by unsupervised neural networks." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300604.

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Yaakob, Abdul Malek Bin. "Multi criteria decision making methodology for fuzzy rule based systems and networks using TOPSIS." Thesis, University of Portsmouth, 2017. https://researchportal.port.ac.uk/portal/en/theses/multi-criteria-decision-making-methodology-for-fuzzy-rule-based-systems-and-networks-using-topsis(8efb475a-4f98-4d5c-846d-f01b01328dda).html.

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Fuzzy systems and networks are vital within the armoury of fuzzy tools and applicable to real life decision making environments. Three types of fuzzy systems introduced in literatures which are systems with single rule base, systems with multiple rule bases and system with networked rule bases. This research introduces novel extension of the Technique of Ordering of Preference by Similarity to Ideal Solution (TOPSIS) methods and uses fuzzy systems and networks to solve multi-criteria decision making problems where both benefit and cost are presented as subsystems. In conjunction, the implement
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Klapil, Ondřej. "Fuzzy systémy s netradičními antecedenty fuzzy pravidel." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220884.

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The aim of this work is to introduce a new type of fuzzy system AnYa. This system, unlike the classical fuzzy systems Takagi-Sugeno and Mamdani, uses a type of antecendent based on real data distribution. As part of the work there will be mentioned system programmed and its functionality will be verified on testing data.
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Arthur, Gerald L. Gong Yang. "Implementation of a fuzzy rule-based decision support system for the immunohistochemical diagnosis of small B-cell lymphomas." Diss., Columbia, Mo. : University of Missouri-Columbia, 2009. http://hdl.handle.net/10355/6569.

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Thesis (M.S.)--University of Missouri-Columbia, 2009.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Thesis advisor: Yang Gong. "May 2009" Includes bibliographical references.
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Karimov, Azar. "Office Rent Variation In Istanbul Cbd: An Application Of Mamdani And Tsk-type Fuzzy Rule Based System." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612322/index.pdf.

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Over the past decade, fuzzy systems have gained remarkable acceptance in many fields including control and automation, pattern recognition, medical diagnosis and forecasting. The fuzzy system application has also been accepted as a promising approach to dealing with uncertainty in real estate valuation analysis. This is mainly due to the necessity of coping with a large number of qualitative and quantitative variables that affect the value of a real property. The appraisers use a great deal of judgment to identify both the characteristics that contribute to property values and the relationsh
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Books on the topic "Fuzzy Rule-based Systems"

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Mendel, Jerry M. Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51370-6.

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Mendel, Jerry M. Explainable Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-35378-9.

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Mendel, Jerry M. Uncertain rule-based fuzzy logic systems: Introduction and new directions. Prentice Hall, 2001.

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Geyer-Schulz, Andreas. Fuzzy rule-based expert systems and genetic machine learning. Physica-Verlag, 1995.

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Angelov, Plamen P. Evolving rule-based models: A tool for design of flexible adaptive systems. Physica-Verlag, 2002.

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Bárdossy, András. Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems. CRC Press, 1995.

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Angelov, Plamen P. Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Physica-Verlag HD, 2002.

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Dubois, Didier. Systèmes à bases de règles floues et leurs applications pour la décision et la commande =: Fuzzy rule-based systems andapplications to decision and control. Institut de Recherche en Informatique de Toulouse, 1991.

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Uncertain Rule-Based Fuzzy Systems. Springer International Publishing AG, 2023.

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Uncertain Rule-Based Fuzzy Systems. Springer Nature, 2017.

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Book chapters on the topic "Fuzzy Rule-based Systems"

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Li, Fangyi, and Qiang Shen. "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Inference Systems." In Fuzzy Rule-Based Inference. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0491-0_1.

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de Barros, Laécio Carvalho, Rodney Carlos Bassanezi, and Weldon Alexander Lodwick. "Fuzzy Rule-Based Systems." In A First Course in Fuzzy Logic, Fuzzy Dynamical Systems, and Biomathematics. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53324-6_5.

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de Barros, Laécio Carvalho, Rodney Carlos Bassanezi, and Weldon A. Lodwick. "Fuzzy Rule-Based Systems." In A First Course in Fuzzy Logic, Fuzzy Dynamical Systems, and Biomathematics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50492-1_5.

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Magdalena, Luis. "Fuzzy Rule-Based Systems." In Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_13.

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Berkachy, Rédina. "Fuzzy Rule-Based Systems." In The Signed Distance Measure in Fuzzy Statistical Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76916-1_3.

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Yager, Ronald R., and Dimitar P. Filev. "Fuzzy Rule Based Models and Approximate Reasoning." In Fuzzy Systems. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5505-6_4.

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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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Mendel, Jerry M. "Type-1 Fuzzy Systems." In Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51370-6_3.

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Shaw, Ian S. "System Identification for Rule-Based Systems." In Fuzzy Control of Industrial Systems. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2813-2_7.

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Mendel, Jerry M. "General Type-2 Fuzzy Systems." In Uncertain Rule-Based Fuzzy Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51370-6_11.

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Conference papers on the topic "Fuzzy Rule-based Systems"

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Haffar, Rami, Francesca Naretto, David Sánchez, Anna Monreale, and Josep Domingo-Ferrer. "GLOR-FLEX: Local to Global Rule-Based EXplanations for Federated Learning." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611878.

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Gonzalez-Garcia, Xabier, Javier Fumanal-Idocin, Joan M. Nunez Do Rio, and Humberto Bustince. "A Rule-Based Approach for Interpretable Intensity-Modulated Radiation Therapy Treatment Selection." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612073.

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Zhou, Mou, Changjing Shang, Pu Zhang, et al. "Towards Rule-ranking Based Fuzzy Rule Interpolation." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494436.

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Zhang, Te, Christian Wagner, and Jonathan M. Garibaldi. "Counterfactual rule generation for fuzzy rule-based classification systems." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882705.

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Baaj, Ismail. "Learning Rule Parameters of Possibilistic Rule-Based System." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882626.

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Kiersztyn, Krystyna, and Adam Kiersztyn. "Fuzzy Rule-based Outlier Detector." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882567.

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Chen, Shyi-Ming, and Yu-Chuan Chang. "Weights-learning for weighted fuzzy rule interpolation in sparse fuzzy rule-based systems." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007479.

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Yeh, I.-Cheng, and Che-hui Lien. "Fuzzy rule-based stock trading system." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007632.

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Mencar, Corrado, and Ciro Castiello. "Descriptive Stability of Fuzzy Rule-Based Systems." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494598.

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Pratama, Mahardhika, Sreenatha G. Anavatti, and Edwin Lughofer. "Evolving fuzzy rule-based classifier based on GENEFIS." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622526.

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