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Journal articles on the topic 'Fuzzy System Models'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Hamdy, Abeer. "Genetic Fuzzy System for Enhancing Software Estimation Models." International Journal of Modeling and Optimization 4, no. 3 (2014): 227–32. http://dx.doi.org/10.7763/ijmo.2014.v4.378.

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2

Yakhyaeva, G. E. "Classes of Fuzzy Models." Bulletin of Irkutsk State University. Series Mathematics 51 (2025): 151–66. https://doi.org/10.26516/1997-7670.2025.51.151.

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The paper is devoted to research in the field of fuzzy model theory. The paper introduces the notion of a coordinated valuation of sentences of a given signature, which can be considered as a generalisation of the notion of a realizable set of sentences to the fuzzy case. By analogy with the classical case, classes of fuzzy models generated by coordinated valuations are considered, and the notion of axiomatised class of fuzzy models is introduced. Fuzzy truth values of different sentences can be considered as a formalisation of subjective evaluative knowledge of experts about object domain. To

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3

Celikyilmaz, A., and I. Burhan Turksen. "Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm." IEEE Transactions on Fuzzy Systems 16, no. 3 (2008): 779–94. http://dx.doi.org/10.1109/tfuzz.2007.905919.

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4

Kramer, Klaus-Dietrich, Annedore Söchting, and Thomas Stolze. "Fuzzy Control Teaching Models." Issues in Informing Science and Information Technology 13 (2016): 225–33. http://dx.doi.org/10.28945/3490.

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Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tu

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5

FILEV, DIMITER. "SYSTEM APPROACH TO DYNAMIC FUZZY MODELS." International Journal of General Systems 21, no. 3 (1992): 311–37. http://dx.doi.org/10.1080/03081079208945084.

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6

Ziyadullaev, Davron, Dilnoz Muhamediyeva, Zafar Abdullaev, Sharofiddin Aynaqulov, and Khasanturdi Kayumov. "Generalized models of a production system of fuzzy conclusion." E3S Web of Conferences 365 (2023): 01019. http://dx.doi.org/10.1051/e3sconf/202336501019.

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The aim of the research is to study the models, rules, and fuzzy inference engines, which occupy the main place in the knowledgebase, and models of the logic inference engines and simulation modeling, focused on supporting the adoption of semi-structured decisions under uncertainty. This implies the relevance of the task of developing theoretical and methodological tools that provide automation of the processes of fuzzy inference systems. Research methods are the theory of fuzzy sets and fuzzy logic. New scientific results are the design and formation of a set of production rules from a given

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7

Thorndike, Alan M. "Fuzzy system models on an IBM PC." ACM SIGSMALL/PC Notes 14, no. 3 (1988): 10–22. http://dx.doi.org/10.1145/49473.49474.

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8

Türksen, I. B., and M. H. Fazel Zarandi. "Fuzzy system models for aggregate scheduling analysis." International Journal of Approximate Reasoning 19, no. 1-2 (1998): 119–43. http://dx.doi.org/10.1016/s0888-613x(98)10003-8.

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9

Zak, S. H. "Stabilizing fuzzy system models using linear controllers." IEEE Transactions on Fuzzy Systems 7, no. 2 (1999): 236–40. http://dx.doi.org/10.1109/91.755404.

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10

Jiang, Hang, Xin Wang, and Li Ming Wang. "Multiple Models Fuzzy PID Controller Design in Ship Power System." Applied Mechanics and Materials 672-674 (October 2014): 1558–61. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1558.

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The ship power system is a complex system with time varying, nonlinear and strong coupling characters. The traditional PID controller is difficult to meet the requirements of the system.In this paper, a multiple models fuzzy PID controller is designed. Considering the different operating conditions of on-land ship power system, two excitation regulation model which respectively corresponds to a fuzzy PID controller are set up. Each time, based on the input operation condition tested, only one fuzzy PID Controller is chosen to improve the control precision. At the end, simulation results show t

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11

ANDRECUT, M., and M. K. ALI. "COMPETITIVE LEARNING OF FUZZY MODELS." International Journal of Modern Physics B 16, no. 30 (2002): 4621–39. http://dx.doi.org/10.1142/s0217979202014863.

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In general, fuzzy modeling requires two stages: structure identification (generating the fuzzy rule base) and parameter learning (optimizing parameters in fuzzy rules). Here, we present an on-line algorithm for competitive learning and optimization of fuzzy models. Differing from existing methods, in this approach the structure identification and parameter optimization of the fuzzy model can be carried out automatically, using on-line acquisition of data. We demonstrate this approach by applying it to different types of nonlinear system modeling.

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12

Türkşen, I. Burhan. "Review of fuzzy system models with an emphasis on fuzzy functions." Transactions of the Institute of Measurement and Control 31, no. 1 (2009): 7–31. http://dx.doi.org/10.1177/0142331208090627.

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13

Campbell, Gwendolyn Elizabeth, Wendi Lynn Buff, and Amy Elizabeth Bolton. "The Diagnostic Utility of Fuzzy System Modeling for Application in Training Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 11 (2000): 370–73. http://dx.doi.org/10.1177/154193120004401111.

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While there are many different computational modeling techniques capable of predicting human decision-making outcomes, training applications require modeling techniques that are also diagnostic of human decision-making processes. Multiple linear regression, a commonly used modeling technique in Psychology, makes overly restrictive processing assumptions such as that of additivity. A relatively new modeling approach, fuzzy system modeling, bears some striking similarities to current theories of categorization and cognition. In this research, we compare the diagnostic utility of multiple linear

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14

Türkşen, I. B. "A review of developments in fuzzy system models: Fuzzy rule bases to fuzzy functions." Scientia Iranica 18, no. 3 (2011): 522–27. http://dx.doi.org/10.1016/j.scient.2011.04.001.

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15

Wang, Hong, and Hong Li. "Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System." Complexity 2022 (April 7, 2022): 1–21. http://dx.doi.org/10.1155/2022/3605881.

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Multisource information systems and multigranulation intuitionistic fuzzy rough sets are important extended types of Pawlak’s classical rough set model. Multigranulation intuitionistic fuzzy rough sets have been investigated in depth in recent years. However, few studies have considered this combination of multisource information systems and intuitionistic fuzzy rough sets. In this paper, we give the uncertainty measure for multisource intuitionistic fuzzy information system. Against the background of multisource intuitionistic fuzzy information system, each information source is regarded as a

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16

Lin, Cheng-Jian. "SISO Nonlinear System Identification Using a Fuzzy-Neural Hybrid System." International Journal of Neural Systems 08, no. 03 (1997): 325–37. http://dx.doi.org/10.1142/s0129065797000331.

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This paper describes a fuzzy-neural hybrid system for the identification of nonlinear dynamic systems with unknown parameters. The proposed model takes the form of a context-sensitive module in which a fuzzy system is used as a function module and a multilayer neural network is used as a context module. Fuzzy-neural hybrid systems with a decomposed structures reduce complexity and thus accelerate the learning process. Also, the parameters of a fuzzy system have clear physical meanings, which makes it possible to incorporate a priori knowledge into the selection of initial parameter values and

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17

Titov, Andrei P. "ANALYSIS OF MODELS OF ADAPTIVE NEURO-FUZZY SYSTEMS." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2024): 21–35. http://dx.doi.org/10.28995/2686-679x-2024-1-21-35.

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The article deals with the study of basic methods for models of adaptive neuro-fuzzy systems. Based on the analysis, the strengths of neural networks and fuzzy logic were found, that became powerful tools for solving complex modeling and forecasting issues. There is studying and analyzing the adaptive neural network, which is a class of neural networks that have the ability to change their structure and parameters in the process of learning and adaptation to new data and conditions and besides the article studies the Gaussian membership function, also known as the normal membership function or

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18

Gennady, Kaniuk, Vasylets Tetiana, Varfolomiyev Oleksiy, Mezerya Andrey, and Antonenko Nataliia. "Development of neuralnetwork and fuzzy models of multimass electromechanical systems." Eastern-European Journal of Enterprise Technologies 3, no. 2(99) (2019): 51–63. https://doi.org/10.15587/1729-4061.2019.169080.

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The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study. A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesiz

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19

Ho, Chun-Yen, Hsien-Keng Chen, and Zheng-Ming Ge. "Chaos synchronization of Yin and Yang T-S fuzzy models of Hénon map system." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 7 (2012): 1535–43. http://dx.doi.org/10.1177/0954406212465236.

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Based on the Chinese philosophy, Yin is the decreasing, negative, historical or feminine principle in nature, while Yang is the increasing, positive, contemporary or masculine principle in nature. Yin and Yang are two fundamental opposites in Chinese philosophy. Since the discrete Hénon map system is an invertible map, Yin–Yang T-S fuzzy model of chaotic Hénon map systems with increasing and decreasing argument can be studied. The Yang T-S fuzzy model of chaotic Hénon map system is presented. The Yin T-S fuzzy model of chaotic Hénon map system and Yin T-S fuzzy rules are obtained by invertible

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20

Majumdar, Abhijit, and Anindya Ghosh. "Yarn Strength Modelling Using Fuzzy Expert System." Journal of Engineered Fibers and Fabrics 3, no. 4 (2008): 155892500800300. http://dx.doi.org/10.1177/155892500800300408.

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Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are traine

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21

Elwakil, Emad, and Tarek Zayed. "Construction productivity fuzzy knowledge base management system." Canadian Journal of Civil Engineering 45, no. 5 (2018): 329–38. http://dx.doi.org/10.1139/cjce-2017-0540.

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Construction companies need a knowledge management system to collate, share and ultimately apply this knowledge in various projects. One of the most important elements that determine the time estimates of any construction project is productivity. Such projects have a predilection towards uncertainty and therefore require new generation of prediction models that utilizes available historical data. The research presented in this paper develops, using fuzzy approach, a knowledge base to analyze, extract and infer any underlying patterns of the data sets to predict the duration and productivity of

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22

Halim, K. A., B. C. Giri, and K. S. Chaudhuri. "Fuzzy EPQ models for an imperfect production system." International Journal of Systems Science 40, no. 1 (2009): 45–52. http://dx.doi.org/10.1080/00207720802090427.

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23

Jiang, Hang, Xin Wang, Yi Hui Zheng, Li Xue Li, and Yan Liu. "Multiple Models Fuzzy PID Controller Design in Air-Condition Temperature Control System." Advanced Materials Research 860-863 (December 2013): 1616–19. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1616.

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This paper deals with the study of air-condition temperature control, which is a complex system of time varying, nonlinear, imprecise model and uncertain work environment. In this paper, a multiple models fuzzy PID controller is designed. Considering the air-condition temperature adjustment system to combat aircraft and the frequently changing external environment the multiple fuzzy PID controllers are estimated according to the temperature changing area. Each time, based on temperature tested, only one fuzzy PID Controller is chosen to improve the control precision. At the end, simulation res

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24

Kawaji, Shigeyasu, and Yuehui Chen. "Evolving Neurofuzzy System by Hybrid Soft Computing Approaches for System Identification." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 4 (2001): 220–28. http://dx.doi.org/10.20965/jaciii.2001.p0220.

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This paper studies optimizing neurofuzzy system using a hybrid approach of a modified probabilistic incremental program evolution algorithm (MPIPE), random search algorithm, and evolutionary programming (EP). Neurofuzzy system is a combination of fuzzy system and neural network. The performance of a neurofuzzy system depends largely on selection of fuzzy membership functions, partition of input space and fuzzy rules. Two neurofuzzy models, additive and direct, are proposed in which neurofuzzy system calculation is based on tree structural representation. Without prior knowledge of the plant, p

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25

Alić, Senad, Safet Brdarević, Sabahudin Jašarević, and Mustafa Imamović. "INTRODUCTION OF EXSPERT SYSTEM ANFIS INTO MAINTENANCE SYSTEM OF PROCESS FANS." Mašinstvo 12, no. 2 (2015): 41–51. https://doi.org/10.62456/jmem.2015.02.041.

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&lt;p style=&quot;text-align: justify;&quot;&gt;Neuro-fuzzy systems represent a modern class of hybrid intelligent systems. They are described as artificial neural networks characterized by fuzzy parameters. The combination of two different concepts of artificial intelligence tries to take of individual advantages of fuzzy logic and artificial neural networks in hybrid systems of homogeneous structure. Such systems are increasingly being used for solving of everyday complex problems. The possibility to display fuzzy models in the form of neural network is commonly used in the p

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26

İnan, Onur, and Ali Özhan Akyüz. "ROBUST FUZZY MODELS FOR ULTRASONIC POLYMER DEGRADATION." Yalvaç Akademi Dergisi 10, no. 1 (2025): 40–51. https://doi.org/10.57120/yalvac.1644658.

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Fuzzy Logic Models are practical solutions to reach a definite conclusion in data sets with uncertain, complicated, and incomplete input data. Owing to these models, achieving the desired outputs with very low error in large data sets obtained theoretically or experimentally is possible. In this study, a subtractive clustering based fuzzy model approach has been presented to analyze the ultrasonic polymer degradation. Fuzzy models include obtaining cluster centers from the data set, preparing a fuzzy rule-based linear equation system, and optimizing parameters for the least error. The designed

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27

Kozhukhivskyi, A. D., and O. A. Kozhukhivska. "RISK ASSESSMENT MODELING OF ERP-SYSTEMS." Radio Electronics, Computer Science, Control, no. 4 (December 13, 2022): 149. http://dx.doi.org/10.15588/1607-3274-2022-4-12.

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Context. Because assessing security risks is a complex and complete uncertainty process, and uncertainties are a major factor influencing valuation performance, it is advisable to use fuzzy methods and models that are adaptive to noncomputed data. The formation of vague assessments of risk factors is subjective, and risk assessment depends on the practical results obtained in the process of processing the risks of threats that have already arisen during the functioning of the organization and experience of security professionals. Therefore, it will be advisable to use models that can ade-quate

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Nebot, Àngela, and Francisco Mugica. "Forest Fire Forecasting Using Fuzzy Logic Models." Forests 12, no. 8 (2021): 1005. http://dx.doi.org/10.3390/f12081005.

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In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful fuzzy techniques for modelling burned areas of forests in Portugal. The results obtained from them were compared with those of other artificial intelligence techniques applied to the same datasets found in the literature.

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Yabuuchi, Yoshiyuki, Takayuki Kawaura, and Junzo Watada. "Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals and its Evaluation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 4 (2016): 512–20. http://dx.doi.org/10.20965/jaciii.2016.p0512.

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Interval models based on fuzzy regression and fuzzy time-series can illustrate the possibilities of a system using the intervals in the model. Thus, the aim is to minimize the vagueness of the model in order to describe the possible states of the system. In the present study, we consider on an interval fuzzy time-series model based on a Box–Jenkins model, a fuzzy autocorrelation model proposed by Yabuuchi, and a fuzzy regressive model proposed by Ozawa. We examine two models by analyzing the Japanese national consumer price index and demonstrate that our approach improves the accuracy of predi

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30

Vergassola, R., W. Zong, M. R. Berthold, and R. Silipo. "Knowledge-based and Data-driven Models in Arrhythmia Fuzzy Classification." Methods of Information in Medicine 40, no. 05 (2001): 397–402. http://dx.doi.org/10.1055/s-0038-1634199.

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Summary Objectives: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem. Methods: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is

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31

Zhang, Bin, and Yung C. Shin. "A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems." Applied Sciences 11, no. 1 (2020): 62. http://dx.doi.org/10.3390/app11010062.

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A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy

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32

Bryniarska, Anna. "Mathematical Models of Diagnostic Information Granules Generated by Scaling Intuitionistic Fuzzy Sets." Applied Sciences 12, no. 5 (2022): 2597. http://dx.doi.org/10.3390/app12052597.

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The paper presents a certain class of the mathematical models of diagnostic information granules describing the fuzzy symptoms-faults relationship. A certain fuzzy diagnostic information retrieval system is described as an application of an expert diagnostic system. Symptoms and faults are fuzzy, and with some scaling of the symptom-fault concept pair values. These value pairs can be considered as intuitionistic fuzzy sets for the space of diagnosed objects. In this article, for scaling intuitionistic fuzzy sets (n-ScIFS), the deductive theory is formulated. There the intuitionistic fuzzy sets

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33

Germashev, Ilya, and Viktoriya Dubovskaya. "Application of Fuzzy Mathematics Models for Solving Medical Diagnostics Problems." Mathematical Physics and Computer Simulation, no. 4 (December 2021): 53–66. http://dx.doi.org/10.15688/mpcm.jvolsu.2021.4.4.

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Fuzzy set theory and fuzzy logic are a very suitable and applicable framework for developing knowledge-based systems in medicine for tasks such as interpreting medical outcome sets, differentiating syndromes, diagnosing various diseases, choosing optimal treatment tactics, and monitoring patients in real time. fuzzy logic is similar to the system of human thinking and, therefore, can cope with the uncertainties and inaccuracies found by the specialist in the course of working with the patient and making a medical diagnosis. The analysis of modern medical practice and literature sources proves

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34

Danylchenko, Dmytro, and Andrii Potryvai. "System for dynamic prediction of the technical condition of the equipment of a combined electric power system." Bulletin of the National Technical University "KhPI". Series: Energy: Reliability and Energy Efficiency, no. 1 (6) (July 9, 2023): 16–21. http://dx.doi.org/10.20998/2224-0349.2023.01.10.

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This paper analyses the neuro-fuzzy modelling method for predicting the technical state of a system. The model of the system for dynamic forecasting of the technical condition of the equipment of an integrated power system consists of: a simulation model of a solar power plant, a simulation model of a wind turbine, and a simulation model of a synchronous generator acting as a thermal power plant. To perform the modelling, a special system of tools Fuzzy extension logic toolbox was used to assess the quality of the synchronous generator. The obtained estimates of the technical condition of the

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35

Yang, Hong-Tzer, and Chao-Ming Huang. "Distribution system service restoration using fuzzy Petri Net models." International Journal of Electrical Power & Energy Systems 24, no. 5 (2002): 395–403. http://dx.doi.org/10.1016/s0142-0615(01)00034-5.

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36

Averkin, A. N., T. V. Agrafonova, and N. V. Titova. "System of decision making support based on fuzzy models." Journal of Computer and Systems Sciences International 48, no. 1 (2009): 84–94. http://dx.doi.org/10.1134/s1064230709010080.

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37

Valencia, Felipe, Doris Saez, Jorge Collado, Fernanda Avila, Alejandro Marquez, and Jairo J. Espinosa. "Robust Energy Management System Based on Interval Fuzzy Models." IEEE Transactions on Control Systems Technology 24, no. 1 (2016): 140–57. http://dx.doi.org/10.1109/tcst.2015.2421334.

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Mammadova, Kifayat, and Asmet Ahmedova. "SYNTHESIS OF A FUZZY INTELLIGENT CONTROL SYSTEM FOR A DYNAMIC OBJECT BASED ON FUZZY LOGIC." InterConf, no. 15(117) (July 20, 2022): 337–46. http://dx.doi.org/10.51582/interconf.19-20.07.2022.036.

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This article discusses the synthesis of a fuzzy dynamic object-driven intelligent control system using a fuzzy mathematical apparatus. If the exact description of the control object is rather complicated and there is no a priori information about the operation of the system, it is considered more important to apply fuzzy intelligent control systems to control objects. Unlike classical models, fuzzy models are more flexible and allow taking into account the experience and intuition of specialists in a particular field. The fuzzy model of control and diagnostics is more adequate to the simulated

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Dhikra, Saoudi. "CONTROLLER DESIGN BASED ON FUZZY OBSERVERS FOR T-S FUZZY BILINEAR MODELS." International Journal of Control Theory and Computer Modeling (IJCTCM) 6, no. 1/2 (2016): 01–16. https://doi.org/10.5121/ijctcm.2016.6201.

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This article is devoted to the design of a fuzzy-observer-based control for a class of nonlinear systems with bilinear terms. The class of systems considered is the Takagi-Sugeno (T-S) fuzzy bilinear model. A new procedure to design the observer-based fuzzy controller for this class of systems is proposed. The aim is to design the fuzzy controller and the fuzzy observer of the augmented system separately in order to guarantee that the error between the state and its estimation converges faster to zero. By using the Lyapunov function, sufficient conditions are derived such that the closed-loop

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40

Turki, Mourad, and Anis Sakly. "Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8942394.

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A new method called cuckoo search (CS) is used to extract and learn the Takagi–Sugeno (T–S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T–S fuzzy model. These parameters are learned simultaneously. The optimized T–S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box–Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained re

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Yang, Shao Zeng, and Jian Hua Zhang. "A Robust Operator Functional State Fuzzy Modeling Approach Based on EEG Data." Applied Mechanics and Materials 556-562 (May 2014): 4065–68. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4065.

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Operator functional state (OFS) is defined as the time-variable ability that an operator completes his/her assigned tasks. To evaluate the OFS in safety-critical human-machine systems, it is modeled by using the Wang-Mendel-based fuzzy system paradigm in this paper. The fuzzy model is constructed to correlate three EEG features (as model inputs) to the human-machine system performance (as model output). To derive a fuzzy model for real-time OFS assessment, the Gaussian membership function membership crossover point membership gradeδis found to be an essential parameter that controls the robust

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Han, Hugang, and Yuta Higaki. "Controller Designs for a Class of Polynomial Fuzzy Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 796–803. http://dx.doi.org/10.20965/jaciii.2015.p0796.

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This paper proposes two polynomial fuzzy controllers in the context of the fuzzy polynomial model with a so-called lumped disturbance. One, called regular controller, is designed only based on the control system stability, while the other, called controller with disturbance observer, is designed on the basis of both control system stability and a disturbance observer proposed in this paper. Though both controllers are able to stabilize the control system, computer simulations conclude that the latter is better than the former from the point of view of the control performance when it comes to t

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Sepúlveda, Carlos, Oscar Montiel, José M. Cornejo Bravo, and Roberto Sepúlveda. "Fuzzy Evaluation of Pharmacokinetic Models." Computational Intelligence and Neuroscience 2018 (November 1, 2018): 1–10. http://dx.doi.org/10.1155/2018/1983897.

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Population pharmacokinetic (PopPK) models allow researchers to predict and analyze drug behavior in a population of individuals and to quantify the different sources of variability among these individuals. In the development of PopPK models, the most frequently used method is the nonlinear mixed effect model (NLME). However, once the PopPK model has been developed, it is necessary to determine if the selected model is the best one of the developed models during the population pharmacokinetic study, and this sometimes becomes a multiple criteria decision making (MCDM) problem, and frequently, r

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Al-Gallaf, E. A. "Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System." Journal of Engineering Research [TJER] 2, no. 1 (2005): 12. http://dx.doi.org/10.24200/tjer.vol2iss1pp12-24.

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This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system und

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Patel, Himanshukumar R., and Vipul A. Shah. "Stable Fault Tolerant Controller Design for Takagi–Sugeno Fuzzy Model-Based Control Systems via Linear Matrix Inequalities: Three Conical Tank Case Study." Energies 12, no. 11 (2019): 2221. http://dx.doi.org/10.3390/en12112221.

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This paper deals with a methodical design approach of fault-tolerant controller that gives assurance for the the stabilization and acceptable control performance of the nonlinear systems which can be described by Takagi–Sugeno (T–S) fuzzy models. Takagi–Sugeno fuzzy model gives a unique edge that allows us to apply the traditional linear system theory for the investigation and blend of nonlinear systems by linear models in a different state space region. The overall fuzzy model of the nonlinear system is obtained by fuzzy combination of the all linear models. After that, based on this linear m

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Sangeeta, Sangeeta, and Naveen Kumar. "Implementing queue models with fuzzy and stochastic." Journal of Advances in Science and Technology 22, no. 1 (2025): 67–77. https://doi.org/10.29070/7wza1x89.

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Uncertainty and imprecision have a major impact on queue performance and decision-making in real-world systems, particularly in industrial, service, and computer contexts. The inherent fuzziness in real-world situations may be lost in traditional stochastic queue models since they rely on exact probability distributions. This research suggests a hybrid model that combines stochastic and fuzzy set theories to assess queue systems in the face of uncertainty, which would solve this shortcoming. In terms of factors like arrival rates, service periods, and system capacity, the method accounts for b

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Milovancevic, Milos, Aleksandar Dimov, Kamen Boyanov Spasov, Ljubomir Vračar, and Miroslav Planić. "Neuro-Fuzzy Evaluation of the Software Reliability Models by Adaptive Neuro Fuzzy Inference System." Journal of Electronic Testing 37, no. 4 (2021): 439–52. http://dx.doi.org/10.1007/s10836-021-05964-y.

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Gao, Peng, Liyang Xie, Wei Hu, Chang Liu, and Jinzhao Feng. "Dynamic Fuzzy Reliability Analysis of Multistate Systems Based on Universal Generating Function." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/6524629.

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Considering the fuzziness of load, strength, operational states, and state probability, reliability models of multistate systems are developed based on universal generating function (UGF). The fuzzy UGF of load and the fuzzy UGF of strength are proposed in this paper, which are used to derive the fuzzy component UGF and the fuzzy system UGF. By defining the decomposition operator and the inner product operator, failure dependence and effects of multiple load applications are taken into account in the established reliability models. Moreover, dynamic fuzzy reliability models of multistate syste

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Akram, Muhammad, Ahmad N. Al-Kenani, and Anam Luqman. "Degree based models of granular computing under fuzzy indiscernibility relations." Mathematical Biosciences and Engineering 18, no. 6 (2021): 8415–43. http://dx.doi.org/10.3934/mbe.2021417.

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<abstract><p>The aim of this research work is to put forward fuzzy models of granular computing based on fuzzy relation and fuzzy indiscernibility relation. Thanks to fuzzy information granulation to provide multi-level visualization of problems that include uncertain information. In such a granulation, fuzzy sets and fuzzy graphs help us to represent relationships among granules, groups or clusters. We consider the fuzzy indiscernibility relation of a fuzzy knowledge representation system ($ \mathcal{I} $). We describe the granular structures of $ \mathcal{I} $, including discerni

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Wang, Xiaowei, Alexander Gegov, Arabikhan Farzad, Yuntao Chen, and Qiwei Hu. "Fuzzy Network Based Framework for Software Maintainability Prediction." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 05 (2019): 841–62. http://dx.doi.org/10.1142/s0218488519500375.

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Software metrics based maintainability prediction is leading to development of new sophisticated techniques to construct prediction models. This paper proposes a new software maintainability prediction framework, which bases on Fuzzy Network, a novel exploratory modeling technique. The proposed framework utilizes both the metric data collected from software system and the subjective appraisals from experts. An application example of the framework is shown. In comparison to the Standard Fuzzy System based models, Fuzzy Network based models improves the transparency more than 71.3% and the accur

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