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Relevant bibliographies by topics / Fuzzy inference systems

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Academic literature on the topic 'Fuzzy inference systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Fuzzy inference systems"

1

Terenchuk, Svitlana, Yuliia Riabchun, and Maksym Delembovskyi. "IDENTIFICATION OF ENTRANT’S ABILITIES ON THE BASIS OF SUGENO-TYPE FUZZY INFERENCE SYSTEMS." Aviation 26, no. 4 (2022): 176–82. http://dx.doi.org/10.3846/aviation.2022.17636.

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In the conditions of effective training in aviation for dispatchers and pilots, it requires the use of infocommunication systems capable of working under conditions of fuzzy uncertainty in real time. The functioning of such systems is based on fuzzy inference systems. However, the development and implementation of these systems requires the creation of fuzzy knowledge bases. Therefore, special attention in this study is paid to the creation of a system of fuzzy inferences and the formation of a fuzzy knowledge base of this system. The result is a lozenge-type fuzzy inference system. The fuzzy

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Romanov, Anton, Julia Stroeva, Aleksey Filippov, and Nadezhda Yarushkina. "An Approach to Building Decision Support Systems Based on an Ontology Service." Mathematics 9, no. 22 (2021): 2946. http://dx.doi.org/10.3390/math9222946.

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Modern decision support systems (DSSs) need components for storing knowledge. Moreover, DSSs must support fuzzy inference to work with uncertainty. Ontologies are designed to represent knowledge of complex structures and to perform inference tasks. Developers must use the OWLAPI and SWRL API libraries to use ontology features. They are impossible to use in DSSs written in programming languages not for Java Virtual Machines. The FuzzyOWL library and the FuzzyDL inference engine are required to work with fuzzy ontologies. The FuzzyOWL library is currently unmaintained and does not have a public

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Mazandarani, Mehran, and Xiu Li. "Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems." IEEE Access 8 (2020): 126066–82. http://dx.doi.org/10.1109/access.2020.3008064.

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Kong, A. "Sparse distributed fuzzy inference systems." Soft Computing 10, no. 7 (2005): 567–77. http://dx.doi.org/10.1007/s00500-005-0518-4.

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Provotar, O. I., and O. O. Provotar. "Credibility in Fuzzy Inference Systems." Cybernetics and Systems Analysis 53, no. 6 (2017): 866–75. http://dx.doi.org/10.1007/s10559-017-9988-5.

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Kerk, Yi Wen, Kai Meng Tay, and Chee Peng Lim. "Monotone Interval Fuzzy Inference Systems." IEEE Transactions on Fuzzy Systems 27, no. 11 (2019): 2255–64. http://dx.doi.org/10.1109/tfuzz.2019.2896852.

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Mazandarani, Mehran, and Li Xiu. "Interval type-2 fractional fuzzy inference systems: Towards an evolution in fuzzy inference systems." Expert Systems with Applications 189 (March 2022): 115947. http://dx.doi.org/10.1016/j.eswa.2021.115947.

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Jacquin, Alexandra P., and Asaad Y. Shamseldin. "Review of the application of fuzzy inference systems in river flow forecasting." Journal of Hydroinformatics 11, no. 3-4 (2009): 202–10. http://dx.doi.org/10.2166/hydro.2009.038.

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This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi–Sugeno–Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective t

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Díaz-Montarroso, Carolina, Nicolás Madrid, and Eloísa Ramírez-Poussa. "Correctness of Fuzzy Inference Systems Based on f-Inclusion." Mathematics 13, no. 11 (2025): 1897. https://doi.org/10.3390/math13111897.

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Recent work has shown that the f-index of inclusion can serve as a foundation for modeling Generalized Modus Ponens. In this paper, we develop a novel fuzzy inference system based on this inference rule. To establish its soundness, we connect it to a Fuzzy Description Logic LU enriched with fuzzy modifiers (also known as fuzzy hedges). This logic background provides to the approach a strength absent in most fuzzy inference systems in the literature, which allows us to formally prove a series of results that culminate in a final correctness theorem for the proposed fuzzy inference system. This

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Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.

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Dissertations / Theses on the topic "Fuzzy inference systems"

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Weinschenk, Jeffrey Joseph. "Complexity reduction in fuzzy inference systems /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5945.

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Xu, Andong. "Flexible adaptive-network-based fuzzy inference system." Diss., Online access via UMI:, 2006.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2006.<br>Includes bibliographical references.

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Lau, Chun Yin. "Extended adapative [i.e. adaptive] neuro-fuzzy inference systems." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20070130.170625/index.html.

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Weeraprajak, Issarest. "Faster Adaptive Network Based Fuzzy Inference System." Thesis, University of Canterbury. Mathematics and Statistics, 2007. http://hdl.handle.net/10092/1234.

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It has been shown by Roger Jang in his paper titled "Adaptive-network-based fuzzy inference systems" that the Adaptive Network based Fuzzy Inference System can model nonlinear functions, identify nonlinear components in a control system, and predict a chaotic time series. The system use hybrid-learning procedure which employs the back-propagation-type gradient descent algorithm and the least squares estimator to estimate parameters of the model. However the learning procedure has several shortcomings due to the fact that * There is a harmful and unforeseeable influence of the size of the pa

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MERINO, JORGE SALVADOR PAREDES. "AUTOMATIC SYNTHESIS OF FUZZY INFERENCE SYSTEMS FOR CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27007@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Hoje em dia, grande parte do conhecimento acumulado está armazenado em forma de dados. Para muitos problemas de classificação, tenta-se aprender a relação entre um conjunto de variáveis (atributos) e uma variável alvo de interesse. Dentre as ferramentas capazes de atuar como modelos representativos de sistemas reais, os Sistemas de Inferência Fuzzy são considerados excelentes co

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Lee, Chang Su. "A framework of adaptive T-S type rough-fuzzy inference systems (ARFIS)." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0192.

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[Truncated abstract] Fuzzy inference systems (FIS) are information processing systems using fuzzy logic mechanism to represent the human reasoning process and to make decisions based on uncertain, imprecise environments in our daily lives. Since the introduction of fuzzy set theory, fuzzy inference systems have been widely used mainly for system modeling, industrial plant control for a variety of practical applications, and also other decisionmaking purposes; advanced data analysis in medical research, risk management in business, stock market prediction in finance, data analysis in bioinforma

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Morphet, Steven Brian Işık Can. "Modeling neural networks via linguistically interpretable fuzzy inference systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2004. http://wwwlib.umi.com/cr/syr/main.

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Hudgins, Billy E. "Implementation of fuzzy inference systems using neural network techniques." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23919.

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RIBEIRO, NICHOLAS PINHO. "HIERARCHICAL FUZZY INFERENCE SYSTEMS APPLIED TO HUMAN RELIABILITY ASSESSMENT." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24722@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>A maioria dos estudos existentes em controle de qualidade de processos focam no desempenho de máquinas e ferramentas. Assim, estes já contam com bons métodos para serem controlados. Contudo, erros humanos em potencial estão presentes em todos os processos industriais que contenham a relação homem-máquina, fazendo com que a necessidade de se avaliar a qualidade do desempenho humano seja de igual importância. A abordagem para se avaliar quão su

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Prajitno, Prawito. "Neuro-fuzzy methods in multisensor data fusion." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251258.

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Books on the topic "Fuzzy inference systems"

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Grabisch, Michel. Fundamentals of uncertainty calculi with applications to fuzzy inference. Kluwer Academic Publishers, 1995.

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Hudgins, Billy E. Implementation of fuzzy inference systems using neural network techniques. Naval Postgraduate School, 1992.

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Bobyr', Maksim, Sergey Emel'yanov, Aleksandr Arhipov, Natal'ya Milostnaya, Andrey Ronzhin, and Roman Mescheryakov. Applied neuro-fuzzy computing systems and devices. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1900641.

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The monograph is devoted to the analysis and development of applied neuro-fuzzy systems and devices. The issues related to the training of neuro-fuzzy inference systems are outlined. There are many examples and algorithms that explain the essence of the functioning of the developed methods. It is intended for students, postgraduates, researchers, engineers engaged in the development of intelligent systems and devices for controlling mechanisms.

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S, Teichrow Jon, University of Houston--Clear Lake. Research Institute for Computing and Information Systems., and Lyndon B. Johnson Space Center. Information Technology Division., eds. Real-time fuzzy inference based robot path planning: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.

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Demri, Stéphane P. Incomplete Information: Structure, Inference, Complexity. Springer Berlin Heidelberg, 2002.

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Shukla, K. K. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. Springer London, 2013.

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Neelanarayanan, ed. Multi-step Prediction of Pathological Tremor With Adaptive Neuro Fuzzy Inference System (ANFIS). Association of Scientists, Developers and Faculties, 2014.

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Hung T. Hung T. Nguyen, E. A. Walker, and Michel Grabisch. Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Springer London, Limited, 2013.

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Nguyen, Hung T., Michel Grabisch, and E. A. Walker. Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Springer, 2010.

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Real-time fuzzy inference based robot path planning: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1990.

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Book chapters on the topic "Fuzzy inference systems"

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Hudec, Miroslav. "Fuzzy Inference." In Fuzziness in Information Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42518-4_4.

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Singh, Himanshu, and Yunis Ahmad Lone. "Fuzzy Inference Systems." In Deep Neuro-Fuzzy Systems with Python. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5361-8_3.

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Rong, Hai-Jun, and Zhao-Xu Yang. "Fuzzy Inference Systems." In Sequential Intelligent Dynamic System Modeling and Control. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1541-1_1.

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Tomasiello, Stefania, Witold Pedrycz, and Vincenzo Loia. "Fuzzy Inference Systems." In Big and Integrated Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98974-3_5.

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Tomasiello, Stefania, Witold Pedrycz, and Vincenzo Loia. "Fuzzy Inference Systems." In Big and Integrated Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98974-3_5.

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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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Novák, Vilém. "Weighted Inference Systems." In Fuzzy Sets in Approximate Reasoning and Information Systems. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5243-7_3.

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León-Rojas, Juan M., and Montaña Morales. "Interval Fuzzy Bayesian Inference." In Soft Methodology and Random Information Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44465-7_69.

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Abraham, A. "Adaptation of Fuzzy Inference System Using Neural Learning." In Fuzzy Systems Engineering. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11339366_3.

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Kasabov, Nikola. "Evolving Neuro-Fuzzy Inference Systems." In Perspectives in Neural Computing. Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3740-5_5.

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Conference papers on the topic "Fuzzy inference systems"

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Acampora, Giovanni, Roberto Schiattarella, and Autilia Vitiello. "Using Quantum Fuzzy Inference Engines in Smart Cities." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611863.

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Pékala, Barbara, Piotr Grochowalskii, Dawid Kosior, et al. "Applications of IFIS python library in interval-valued fuzzy inference problems." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612169.

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H Gedara, T. Milinda, Vincenzo Loia, and Stefania Tomasiello. "Detecting Fake Images Using Neuro-Fuzzy Inference Systems: A Brief Comparative Analysis." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612033.

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Garibaldi, Jonathan M., and Christian Wagner. "L-fuzzy inference." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891803.

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Rezaee-Ahmadi, Fatemeh, Hamed Rafiei, and Mohammad-R. Akbarzadeh-T. "Z-Adaptive Fuzzy Inference Systems." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494413.

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Seki, Hirosato, and Masaharu Mizumoto. "Fuzzy functional inference method." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584906.

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Pourabdollah, Amir, Colin Wilmott, Roberto Schiattarella, and Giovanni Acampora. "Fuzzy Inference on Quantum Annealers." In 2023 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, 2023. http://dx.doi.org/10.1109/fuzz52849.2023.10309732.

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Carniel, Anderson Chaves, Felippe Galdino, and Markus Schneider. "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882658.

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Seki, Hirosato, and Masaharu Mizumoto. "Fuzzy singleton-type SIC fuzzy inference model." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622583.

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Bisgambiglia, P. A., L. Capocchi, P. Bisgambiglia, and S. Garredu. "Fuzzy inference models for Discrete EVent systems." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584707.

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Reports on the topic "Fuzzy inference systems"

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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

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Reveiz-Herault, Alejandro, and Carlos Eduardo León-Rincón. Operational risk management using a fuzzy logic inference system. Banco de la República, 2009. http://dx.doi.org/10.32468/be.574.

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Morkun, Volodymyr, Natalia Morkun, Andrii Pikilnyak, Serhii Semerikov, Oleksandra Serdiuk, and Irina Gaponenko. The Cyber-Physical System for Increasing the Efficiency of the Iron Ore Desliming Process. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4373.

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It is proposed to carry out the spatial effect of high-energy ultrasound dynamic effects with controlled characteristics on the solid phase particles of the ore pulp in the deslimer input product to increase the efficiency of thickening and desliming processes of iron ore beneficiation products. The above allows predicting the characteristics of particle gravitational sedimentation based on an assessment of the spatial dynamics of pulp solid- phase particles under the controlled action of high-energy ultrasound and fuzzy logical inference. The object of study is the assessment of the character

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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 infer

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