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Relevant bibliographies by topics / Fuzzy linguistic modelling

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Academic literature on the topic 'Fuzzy linguistic modelling'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Fuzzy linguistic modelling"

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Ramachandran, V., V. Sankaranarayanan, and S. Seshasayee. "Fuzzy reliability modelling — Linguistic approach." Microelectronics Reliability 32, no. 9 (1992): 1311–18. http://dx.doi.org/10.1016/0026-2714(92)90654-4.

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Andreea V, Cojocaru, and Balint Stefan. "Systems of Fuzzy Reasoning and Expert Knowledge-based Fuzzy Reasoning Systems, used in Modelling Real-world Phenomena." International Journal of Physics Research and Applications 8, no. 6 (2025): 156–68. https://doi.org/10.29328/journal.ijpra.1001124.

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In this paper, the concept of ‘system of fuzzy reasoning’ is presented. It is the first temptation to expand ideas presented in [1] concerning ‘human intelligence’ linguistic variable and [2] concerning ‘truth value of fuzzy reasoning’ in the framework of the human intelligence linguistic variable. In ‘systems of fuzzy reasoning’, besides the ‘human intelligence’ linguistic variable also other linguistic variables are also used. An expert knowledge-based system of fuzzy reasoning describing the dynamics of a real-world phenomenon is presented.

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Martínez-Jiménez, Pedro Manuel, Jesús Chamorro-Martínez, and José Manuel Soto-Hidalgo. "Perception-based fuzzy partitions for visual texture modelling." Fuzzy Sets and Systems 337 (April 15, 2018): 1–24. https://doi.org/10.1016/j.fss.2017.04.015.

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Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like ``very coarse'', ``low directional'', or ``high contrasted''. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels

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Martínez-Jiménez, Pedro Manuel, Jesús Chamorro-Martínez, and José Manuel Soto-Hidalgo. "Perception-based fuzzy partitions for visual texture modelling." Fuzzy Sets and Systems 337 (April 15, 2018): 1–24. https://doi.org/10.5281/zenodo.10575270.

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Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like ``very coarse'', ``low directional'', or ``high contrasted''. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels

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Morente-Molinera, J. A., I. J. Pérez, R. Ureña, and E. Herrera-Viedma. "On Multi-granular Fuzzy Linguistic Modelling in Decision Making." Procedia Computer Science 55 (2015): 593–602. http://dx.doi.org/10.1016/j.procs.2015.07.049.

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CHAKRABORTY, CHANDAN, and DEBJANI CHAKRABORTY. "FUZZY LINEAR AND POLYNOMIAL REGRESSION MODELLING OF ‘IF-THEN’ FUZZY RULEBASE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (2008): 219–32. http://dx.doi.org/10.1142/s0218488508005145.

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In developing so called fuzzy expert systems, fuzzy rule bases have been considered with greater importance. In fact, a fuzzy rule base is a knowledgebase that models human cognitive factors. Fuzzy rules are linguistic ‘IF-THEN’ constructions where ‘IF’ part consists of a set of fuzzy variables and ‘THEN’ part includes a dependent fuzzy variable. In order to identify the underlying mathematical structure in the fuzzy rule base, we develop fuzzy linear and fuzzy polynomial regression techniques in this paper. And the estimation of model parameters is also shown using least-square approach. Fina

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Mengall, G. "Fuzzy modelling for aircraft dynamics identification." Aeronautical Journal 105, no. 1051 (2001): 551–55. http://dx.doi.org/10.1017/s0001924000018029.

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A new methodology is described to identify aircraft dynamics and extract the corresponding aerodynamic coefficients. The proposed approach makes use of fuzzy modelling for the identification process where input/output data are first classified by means of the concept of fuzzy clustering and then the linguistic rules are extracted from the fuzzy clusters. The fuzzy rule-based models are in the form of affine Takagi-Sugeno models, that are able to approximate a large class of nonlinear systems. A comparative study is performed with existing techniques based on the employment of neural networks,

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Farana, Radim, Bogdan Walek, Michal Janošek, and Jaroslav Žáček. "Use of Linguistic Fuzzy-Logic Control for Mechatronic System Modelling and Control." Applied Mechanics and Materials 816 (November 2015): 3–8. http://dx.doi.org/10.4028/www.scientific.net/amm.816.3.

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The article presents use of a linguistic fuzzy-logic control (LFLC) system for mechatronic system modelling and control. The presented applications were verified on real laboratory tasks in the Laboratory of Intelligent Systems at the University of Ostrava. The LFLC system was developed at the University of Ostrava, Institute for Research and Applications of Fuzzy Modeling. This technology enables users to describe the system behaviour and/or the control strategy as a set of fuzzy rules. Input and output variables scales are defined by contexts and their change allows using the same system des

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Labella, Álvaro, Rosa M. Rodríguez, Ahmad A. Alzahrani, and Luis Martínez. "A Consensus Model for Extended Comparative Linguistic Expressions with Symbolic Translation." Mathematics 8, no. 12 (2020): 2198. http://dx.doi.org/10.3390/math8122198.

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Consensus Reaching Process (CRP) is a necessary process to achieve agreed solutions in group decision making (GDM) problems. Usually, these problems are defined in uncertain contexts, in which experts do not have a full and precise knowledge about all aspects of the problem. In real-world GDM problems under uncertainty, it is usual that experts express their preferences by using linguistic expressions. Consequently, different methodologies have modelled linguistic information, in which computing with words stands out and whose basis is the fuzzy linguistic approach and their extensions. Even t

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Chislov, O., N. Lyabakh, M. Kolesnikov, M. Bakalov, and D. Bezusov. "Fuzzy modelling of the transportation logistics processes." Journal of Physics: Conference Series 2131, no. 3 (2021): 032007. http://dx.doi.org/10.1088/1742-6596/2131/3/032007.

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Abstract The relevance of mechanisms and fuzzy modeling methods used in transport and logistics processes is justified. The logic of the transport and logistics chains study by their decomposition into separate economic entities with subsequent synthesis, with taking into account their conflicting interests, is presented. The task of managing transport and logistics processes is set within the framework of the conceptual positions of the fuzzy sets theory: the use of linguistic variables, the concept of a fuzzy set, fuzzy inference, implemented on the basis of fuzzy sets operations. In this st

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Book chapters on the topic "Fuzzy linguistic modelling"

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Vennila, B., and C. Antony Crispin Sweety. "Analyzing an M/M/c Queue with Linguistic Single-Valued Neutrosophic Logic Using Parametric Nonlinear Programming." In Data-Driven Modelling with Fuzzy Sets. CRC Press, 2024. http://dx.doi.org/10.1201/9781003487104-3.

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Kumar, Kamal, Naveen Mani, Amit Sharma, and Reeta Bhardwaj. "A Novel Entropy Measure for Linguistic Intuitionistic Fuzzy Sets and Their Application in Decision-Making." In Multi-Criteria Decision Modelling. CRC Press, 2021. http://dx.doi.org/10.1201/9781003125150-7-7.

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Ishibuchi, Hisao, and Takashi Yamamoto. "Interpretability Issues in Fuzzy Genetics-Based Machine Learning for Linguistic Modelling." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39906-3_11.

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Huseyinov, Ilham N. "Fuzzy Linguistic Modelling Cognitive / Learning Styles for Adaptation through Multi-level Granulation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21619-0_6.

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Zhang, Shengli, and Jing Chen. "Modelling of Fuzzy Discrete Event Systems Based on a Generalized Linguistic Variable and Their Generalized Possibilistic Kriple Structure Representation." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_50.

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Vijayalakshmi, Ponnan, Muthukumaran Harikumaran, N. Vaishnavi, V. Srinath, K. Karan, and T. Joe Jerin. "Optimizing corrosion prevention strategies through mathematical predictive modelling: Integration of linguistic rules and fuzzy inference systems." In Computational Methods in Science and Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003561651-54.

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Huseyinov, Ilham N. "Fuzzy Linguistic Modelling in Multi Modal Human Computer Interaction." In Speech, Image, and Language Processing for Human Computer Interaction. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0954-9.ch004.

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The purpose of this chapter is to explore fuzzy logic based methodology for computing an adaptive interface in an environment of imperfect, vague, multimodal, complex nonlinear hyper information space. To this end, based on fuzzy linguistic modelling and fuzzy multi level granulation an adaptation strategy to cognitive/learning styles is presented. The granulated fuzzy if-then rules are utilized to adaptively map cognitive/learning styles of users to their information navigation and presentation preferences through natural language expressions. The important implications of this approach are t

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Kakar Manish. "Respiratory motion prediction: A fuzzy logic approach." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2011. https://doi.org/10.3233/978-1-60750-754-3-108.

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In this study, lung tumour motion modelling and prediction was done using a fuzzy logic approach. The surrogate signal for breathing motion was obtained from 10 different instances of a lung patient by using signals from Realtime Respiratory Motion Management (RPM) system. A regularity criterion (RC criterion) was used to select appropriate inputs for the model for each trace. A first order Sugeno type model was devised by using a subtractive clustering approach. On an average, the prediction error was seen to be 0.21 mm for training and 0.23 mm for testing. The two main advantages of using a

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Bernabé-Moreno Juan, Tejeda-Lorente Alvaro, Herce-Zelaya Julio, Porcel Carlos, and Herrera-Viedma Enrique. "An Embeddings Based Fuzzy Linguistics Supported Model to Measure the Contextual Bias in Sentiment Polarity." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-900-3-735.

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Polarity detection plays a pivotal role in the modern cognitive research field. Common approaches to compute the polarity of a given word rely on experimental dictionaries providing always the same value, no matter where the word is used and lacking therefore adaptivity to particular contexts. In a previous article, we proposed a method supported by fuzzy linguistic modelling to quantify this contextual bias and to enable the bias-aware sentiment analysis. In this work, we implement the bias contextualization based on a word embeddings technique to capture a larger portion of the contextual bi

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Belousov, Konstantin, and Ivan Labutin. "Social Media User Profiling Based on Genre Extraction." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210207.

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The task of automatic user profiling (in particular, determining their psychological parameters from their texts) in Social Networking Services (SNS) is of great practical importance in many fields (PR and marketing, advertising, politics, social relations and recommendations, etc.). However, this problems’ solution is often complicated by the need to process large amounts of data and the inability to explain the results achieved. Our article presents a new extensible fuzzy classification method for social media user profiling based on preliminary expert analysis of the linguistic behavior of

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Conference papers on the topic "Fuzzy linguistic modelling"

1

Barazane, L., A. Khwaldeh, M. Jumah, and R. Ouiguini. "Linguistic fuzzy modelling of an asynchronous motor." In 2007 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) and Electromotion '07. IEEE, 2007. http://dx.doi.org/10.1109/acemp.2007.4510595.

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Tyagi, Sanjay K., Sujeet K. Sharma, and Vinod Kumar Shukla. "Interpretive Structural Modelling Using Fuzzy Linguistic Information." In 2019 4th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2019. http://dx.doi.org/10.1109/iscon47742.2019.9036267.

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Morente-Molinera, J. A., J. Mezei, C. Carlsson, and E. Herrera-Viedma. "Using multi-granular fuzzy linguistic modelling methods for supervised classification learning purposes." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015406.

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TRUCK, ISIS, NESRIN HALOUANI, and SOUHAIL JEBALI. "LINGUISTIC NEGATION AND 2-TUPLE FUZZY LINGUISTIC REPRESENTATION MODEL: A NEW PROPOSAL." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0016.

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Marquez, Antonio A., Francisco A. Marquez, and Antonio Peregrin. "An efficient multi-objective evolutionary adaptive conjunction for high dimensional problems in linguistic fuzzy modelling." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251181.

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LIU, XIN, XUEWEI TAN, XIAONAN LI, YUNXIA ZHANG, and LI ZOU. "AN ASSESSMENT APPROACH WITH LINGUISTIC TRUTH-VALUED INTUITIONISTIC FUZZY REASONING." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0008.

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Fazili, Furqan. "Linguistic Fuzzy Modelling and Simulation for Fuzzy Logic Controller for Digital to Analog Conversion." In 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2014. http://dx.doi.org/10.1109/cicn.2014.223.

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Gerogiannis, Vassilis C., Elli Rapti, Anthony Karageorgos, and Panos Fitsilis. "Human Resource Assessment in Software Development Projects Using Fuzzy Linguistic 2-Tuples." In 2014 2nd International Conference on Artificial Intelligence, Modelling & Simulation (AIMS). IEEE, 2014. http://dx.doi.org/10.1109/aims.2014.15.

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Morente-Molinera, Juan Antonio, Ignacio Javier Perez, Francisco Javier Cabrerizo, Carlos Porcel, and Enrique Herrera-Viedma. "Improving queries and representing heterogeneous information in Fuzzy Ontologies using multi-granular fuzzy linguistic modelling methods." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2016. http://dx.doi.org/10.1109/smc.2016.7844256.

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ZHANG, YUNXIA, DEGEN HUANG, LI ZOU, and YING WANG. "RESEARCH ON MODUS PONENS AND MODUS TOLLENS ON LINGUISTIC-VALUED INTUITIONISTIC FUZZY LATTICE IMPLICATION ALGEBRA." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0086.

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