Relevant bibliographies by topics / Fuzzy system

Contents

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

Academic literature on the topic 'Fuzzy system'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Fuzzy system"

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Chang, Te-Chuan, C. William Ibbs, and Keith C. Crandall. "A fuzzy logic system for expert systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2, no. 3 (August 1988): 183–93. http://dx.doi.org/10.1017/s0890060400000640.

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Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.
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SUZUKI, Kazuyuki. "Fuzzy System and Fuzzy Modeling." JOURNAL OF THE BREWING SOCIETY OF JAPAN 86, no. 4 (1991): 239–44. http://dx.doi.org/10.6013/jbrewsocjapan1988.86.239.

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U. Kulkarni, Akshay, Amit M. Potadar, Amogh R. Gaonkar, and Amresh Kumar. "Fuzzy Logic Based Career Guidance System." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 01–04. http://dx.doi.org/10.9756/bijsesc.8230.

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Kim, Daijin. "Improving the fuzzy system performance by fuzzy system ensemble." Fuzzy Sets and Systems 98, no. 1 (August 1998): 43–56. http://dx.doi.org/10.1016/s0165-0114(96)00356-9.

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Aly, S., and I. Vrana. "Multiple parallel fuzzy expert systems utilizing a hierarchical fuzz model." Agricultural Economics (Zemědělská ekonomika) 53, No. 2 (January 7, 2008): 89–93. http://dx.doi.org/10.17221/1425-agricecon.

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Business, economic, and agricultural YES-or-NO decision making problems often require multiple, different and specific expertises. This is due to the nature of such problems in which decisions may be influenced by multiple different, relevant aspects, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertises due to its capability to model real world values, which are not always exact, but frequently vague or uncertain. In this research, different expertises, relevant to the decision solution, are modeled using several corresponding FESs. Every FES produces a crisp numerical output expressing the degree of bias toward “Yes” or “No“ decision. A unified scale is standardized for numerical outputs of all FESs. This scale ranges from 0 to 10, where the value 0 represents a complete bias ”No“ decision and the value 10 represents a complete bias to ”Yes“ decision. Intermediate values reflect the degree of bias either to ”Yes“ or ”No“ decision. These systems are then integrated to comprehensibly judge the binary decision problem, which requires all such expertises. Practically, the main reasons for independency among the multiple FESs can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. The proposed mechanism for realizing integration is a hierarchical fuzzy system (HFS) based model, which allows the utilization of the existing If-then knowledge about how to combine/aggregate the outputs of FESs.
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Bamrungsetthapong, Wimonmas, and Adisak Pongpullponsak. "Hybrid Fuzzy Estimation of System Reliability for Multi-State System." Applied Mechanics and Materials 866 (June 2017): 387–91. http://dx.doi.org/10.4028/www.scientific.net/amm.866.387.

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This article is purpose a hybrid estimation of the fuzzy system reliability for the Non-repairable multi-state series-parallel system (NMSS). Considering the fuzzy parameter of NMSS are prior fuzzy parameters. Then the posterior fuzzy parameters of NMSS are constructed by fuzzy Bayesian point estimate of fuzzy system reliability. Moreover, an approach to construct interval estimation of the fuzzy system reliability of NMSS will be used in estimation of the prior fuzzy confidence interval and posterior fuzzy confidence interval of fuzzy system reliability. Finally, the coverage probability and the expected length that it is used to interpret the efficiency of both fuzzy confidence intervals are presented.
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Gammoudi, Aymen, Allel Hadjali, and Boutheina Ben Yaghlane. "Fuzz-TIME: an intelligent system for managing fuzzy temporal information." International Journal of Intelligent Computing and Cybernetics 10, no. 2 (June 12, 2017): 200–222. http://dx.doi.org/10.1108/ijicc-09-2016-0036.

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Purpose Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is subjective and fuzzy. The purpose of this paper is to address the issue related to the modeling and handling of imperfection inherent to both temporal relations and intervals. Design/methodology/approach On the one hand, fuzzy extensions of Allen temporal relations are investigated and, on the other hand, extended temporal relations to define the positions of two fuzzy time intervals are introduced. Then, a database system, called Fuzzy Temporal Information Management and Exploitation (Fuzz-TIME), is developed for the purpose of processing fuzzy temporal queries. Findings To evaluate the proposal, the authors have implemented a Fuzz-TIME system and created a fuzzy historical database for the querying purpose. Some demonstrative scenarios from history domain are proposed and discussed. Research limitations/implications The authors have conducted some experiments on archaeological data to show the effectiveness of the Fuzz-TIME system. However, thorough experiments on large-scale databases are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria. Practical implications The tool developed (Fuzz-TIME) can have many practical applications where time information has to be dealt with. In particular, in several real-world applications like history, medicine, criminal and financial domains, where time is often perceived or expressed in an imprecise/fuzzy manner. Social implications The social implications of this work can be expected, more particularly, in two domains: in the museum to manage, exploit and analysis the piece of information related to archives and historic data; and in the hospitals/medical organizations to deal with time information inherent to data about patients and diseases. Originality/value This paper presents the design and characterization of a novel and intelligent database system to process and manage the imperfection inherent to both temporal relations and intervals.
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Echauz, J. R., and G. J. Vachtsevanos. "Fuzzy Grading System." IEEE Transactions on Education 38, no. 2 (May 1995): 158–65. http://dx.doi.org/10.1109/13.387218.

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Maraj, Elda, and Shkelqim Kuka. "Application of Fuzzy Logic in Traffic System." International Journal of Innovative Research in Engineering & Management 6, no. 4 (July 2019): 33–37. http://dx.doi.org/10.21276/ijirem.2019.6.4.2.

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V, Dharun. "A Comprehensive Study on Fuzzy Logic System." International Journal of Research Publication and Reviews 4, no. 4 (April 2023): 2430–32. http://dx.doi.org/10.55248/gengpi.4.423.36116.

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Dissertations / Theses on the topic "Fuzzy system"

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Chen, Guiming. "Fuzzy FOIL: A fuzzy logic based inductive logic programming system." Thesis, University of Ottawa (Canada), 1996. http://hdl.handle.net/10393/9621.

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In many domains, characterizations of a given attribute are imprecise, uncertain and incomplete in the available learning examples. The definitions of classes may be vague. Learning systems are frequently forced to deal with such uncertainty. Traditional learning systems are designed to work in the domains where imprecision and uncertainty in the data are absent. Those learning systems are limited because of their impossibility to cope with uncertainty--a typical feature of real-world data. In this thesis, we developed a fuzzy learning system which combines inductive learning with a fuzzy approach to solve problems arising in learning tasks in the domains affected by uncertainty and vagueness. Based on Fuzzy Logic, rather than pure First Order Logic used in FOIL, this system extends FOIL with learning fuzzy logic relation from both imprecise examples and background knowledge represented by Fuzzy Prolog. The classification into the positive and negative examples is allowed to be a degree (of positiveness or negativeness) between 0 and 1. The values of a given attribute in examples need not to be the same type. Symbolic and continuous data can exist in the same attribute, allowing for fuzzy unification (inexact matching). An inductive learning problem is formulated as to find a fuzzy logic relation with a degree of truth, in which a fuzzy gain calculation method is used to guide heuristic search. The Fuzzy FOIL's ability of learning the required fuzzy logic relations and dealing with vague data enhances FOIL's usefulness.
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Machová, Kamila. "Vyhodnocení investic s využitím fuzzy logiky." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2017. http://www.nusl.cz/ntk/nusl-318283.

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Master ’s thesis deals with evaluation of investment using advanced methods of analysis and modeling with regards to the best offer. The content of the thesis consists of creating two decision making systems based on fuzzy logic. The thesis also contains a theoretical basics which are necessary for the creation of both systems and evaluating the benefits of the solution.
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Baise, Paul. "Cogitator : a parallel, fuzzy, database-driven expert system." Thesis, Rhodes University, 1994. http://hdl.handle.net/10962/d1006684.

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The quest to build anthropomorphic machines has led researchers to focus on knowledge and the manipulation thereof. Recently, the expert system was proposed as a solution, working well in small, well understood domains. However these initial attempts highlighted the tedious process associated with building systems to display intelligence, the most notable being the Knowledge Acquisition Bottleneck. Attempts to circumvent this problem have led researchers to propose the use of machine learning databases as a source of knowledge. Attempts to utilise databases as sources of knowledge has led to the development Database-Driven Expert Systems. Furthermore, it has been ascertained that a requisite for intelligent systems is powerful computation. In response to these problems and proposals, a new type of database-driven expert system, Cogitator is proposed. It is shown to circumvent the Knowledge Acquisition Bottleneck and posess many other advantages over both traditional expert systems and connectionist systems, whilst having non-serious disadvantages.
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Angstenberger, Larisa. "Dynamic fuzzy pattern recognition." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962701106.

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Castro, León Iván. "Type-2 fuzzy logic system applications for power systems." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3816.

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In the move towards ubiquitous information & communications technology, an opportunity for further optimisation of the power system as a whole has arisen. Nonetheless, the fast growth of intermittent generation concurrently with markets deregulation is driving a need for timely algorithms that can derive value from these new data sources. Type-2 fuzzy logic systems can offer approximate solutions to these computationally hard tasks by expressing non-linear relationships in a more flexible fashion. This thesis explores how type-2 fuzzy logic systems can provide solutions to two of these challenging power system problems; short-term load forecasting and voltage control in distribution networks. On one hand, time-series forecasting is a key input for economic secure power systems as there are many tasks that require a precise determination of the future short-term load (e.g. unit commitment or security assessment among others), but also when dealing with electricity as commodity. As a consequence, short-term load forecasting becomes essential for energy stakeholders and any inaccuracy can be directly translated into their financial performance. All these is reflected in current power systems literature trends where a significant number of papers cover the subject. Extending the existing literature, this work focuses in how these should be implemented from beginning to end to bring to light their predictive performance. Following this research direction, this thesis introduces a novel framework to automatically design type-2 fuzzy logic systems. On the other hand, the low-carbon economy is pushing the grid status even closer to its operational limits. Distribution networks are becoming active systems with power flows and voltages defined not only by load, but also by generation. As consequence, even if it is not yet absolutely clear how power systems will evolve in the long-term, all plausible future scenarios claim for real-time algorithms that can provide near optimal solutions to this challenging mixed-integer non-linear problem. Aligned with research and industry efforts, this thesis introduces a scalable implementation to tackle this task in divide-and-conquer fashion.
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Meng, Zhaojun. "Fuzzy system based voltage ranking for power system network." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401495.

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Lee, James X. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq26881.pdf.

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Lee, James X. (James Xiang) Carleton University Dissertation Engineering Mechanical and Aerospace. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Ottawa, 1997.

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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 partial derivative on the weight step in the back-propagation-type gradient descent algorithm. *In some cases the matrices in the least square estimator can be ill-conditioned. *Several estimators are known which dominate, or outperform, the least square estimator. Therefore this thesis develops a new system that overcomes the above problems, which is called the "Faster Adaptive Network Fuzzy Inference System" (FANFIS). The new system in this thesis is shown to significantly out perform the existing method in predicting a chaotic time series , modelling a three-input nonlinear function and identifying dynamical systems. We also use FANFIS to predict five major stock closing prices in New Zealand namely Air New Zealand "A" Ltd., Brierley Investments Ltd., Carter Holt Harvey Ltd., Lion Nathan Ltd. and Telecom Corporation of New Zealand Ltd. The result shows that the new system out performed other competing models and by using simple trading strategy, profitable forecasting is possible.
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Hu, Cheng Lin. "Design optimization of fuzzy models in system identification." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2493501.

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Books on the topic "Fuzzy system"

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L, Trubatch Sheldon, ed. Fuzzy systems design principles: Building Fuzzy IF-THEN rule bases. New York: IEEE Press, 1997.

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Chakraverty, Snehashish, Smita Tapaswini, and Diptiranjan Behera. Fuzzy Arbitrary Order System. Hoboken, New Jersey: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119004233.

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Moti, Schneider, ed. Fuzzy expert system tools. Chichester [England]: John Wiley, 1996.

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Fuzzy control and identification. Hoboken, N.J: Wiley, 2010.

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United States. National Aeronautics and Space Administration., ed. Learning fuzzy logic control system. [Washington, DC: National Aeronautics and Space Administration, 1994.

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Dongbo, Liu, ed. A fuzzy PROLOG database system. Taunton, Somerset, England: Research Studies Press, 1990.

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Klir, George J. Fuzzy sets, uncertainty, and information. London: Prentice Hall International, 1988.

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Klir, George J. Fuzzy sets, uncertainty, and information. Englewood Cliffs, N.J: Prentice Hall, 1988.

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1953-, Pedrycz Witold, ed. Fuzzy modelling: Paradigms and practice. Boston: Kluwer Academic Publishers, 1996.

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A, Folger Tina, ed. Fuzzysets, uncertainty and information. Englewood Cliffs, NJ: Prentice Hall, 1988.

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Book chapters on the topic "Fuzzy system"

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Nelles, Oliver. "Fuzzy and Neuro-Fuzzy Models." In Nonlinear System Identification, 299–340. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_11.

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Nelles, Oliver. "Fuzzy and Neuro-Fuzzy Models." In Nonlinear System Identification, 347–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_12.

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Bandyopadhyay, Susmita. "Fuzzy Theory and Fuzzy Logic." In Decision Support System, 53–62. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003307655-5.

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Chapman, William, and Arjab Singh Khuman. "Water Carbonation Fuzzy Inference System." In Fuzzy Logic, 253–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_15.

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Pawar, Prashant M., and Ranjan Ganguli. "Genetic Fuzzy System." In Structural Health Monitoring Using Genetic Fuzzy Systems, 25–40. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-907-9_2.

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Terán Tamayo, Luis Fernando. "Fuzzy Recommender System." In SmartParticipation, 47–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06551-9_5.

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Norris, Donald J. "Fuzzy Logic System." In Beginning Artificial Intelligence with the Raspberry Pi, 111–43. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2743-5_5.

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Tan, Joey Sing Yee, and Amandeep S. Sidhu. "Fuzzy Inference System." In Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation, 49–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15585-8_4.

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Egwaikhide, Izebe O. "Fuzzy Modeling of Uncertainty in a Decision Support System for Electric Power System Planning." In Fuzzy Control, 387–96. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_35.

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Caponetto, Riccardo, Mario Lavorgna, Luigi Occhipinti, and GianGuido Rizzotto. "Fuzzy Cellular System: Characteristics and Architecture." In Fuzzy Hardware, 295–309. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4090-8_14.

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Conference papers on the topic "Fuzzy system"

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Schwung, Andreas, and Jurgen Adamy. "Nonlinear system modeling via hybrid system representation of recurrent fuzzy systems." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584751.

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Berenji, Hamid, and Mo Jamshidi. "Fuzzy reinforcement learning for System of Systems (SOS)." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007325.

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Das, Sujit, Pijush Kanti Ghosh, and Samarjit Kar. "Hypertension diagnosis: A comparative study using fuzzy expert system and neuro fuzzy system." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622434.

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Arnett, Timothy, Kelly Cohen, Matthew Clark, David W. Casbeer, and Kuldip Rattan. "Transformation of a hierarchical mamdani fuzzy system to a single fuzzy system representation." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015597.

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Valeev, Sagit, and Natalya Kondratyeva. "Technical safety system with self-organizing sensor system and fuzzy decision support system." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337962.

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Ohtake, Hiroshi, Sho Machida, Kazuo Tanaka, and Hua O. Wang. "A descriptor system approach to servo control for nonlinear systems." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251284.

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Zeng, Xiao-Jun. "A comparison between T-S fuzzy systems and affine T-S fuzzy systems as nonlinear control system models." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891856.

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Chung, I.-Fang, Chia-Feng Juang, and Cheng-Da Hsieh. "Support Vector-trained Recurrent Fuzzy System." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584494.

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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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Yang, Longzhi, Jie Li, Gerhard Fehringer, Phoebe Barraclough, Graham Sexton, and Yi Cao. "Intrusion detection system by fuzzy interpolation." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015710.

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Reports on the topic "Fuzzy system"

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Raychev, Nikolay. Hybrid system with fuzzy hierarchical evaluation model. Web of Open Science, June 2020. http://dx.doi.org/10.37686/nal.v1i1.40.

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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, June 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 to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
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Reveiz-Herault, Alejandro, and Carlos Eduardo León-Rincón. Operational risk management using a fuzzy logic inference system. Bogotá, Colombia: Banco de la República, September 2009. http://dx.doi.org/10.32468/be.574.

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Buyak, Bogdan B., Ivan M. Tsidylo, Victor I. Repskyi, and Vitaliy P. Lyalyuk. Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2669.

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The article describes the problem of designing a neuro-fuzzy expert system of professional selection at the stages of conceptualization and formalization, which involves the definition of concepts, relationships and management mechanisms necessary to describe the solution of problems in the chosen subject field. The structural model of the decision making system for determining the professional selection of students for training in IT specialties is substantiated. Three subsystems are proposed as structural components for studying: psychological peculiarities, personal qualities, factual knowledge, abilities and skills of students. The quality of the system’s operation is determined by the use of various techniques for acquiring knowledge on the basis of which the knowledge base of the neuro-fuzzy system and the combination of the use of fuzzy and stochastic data will be formed.
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Seale, Maria, R. Salter, Natàlia Garcia-Reyero,, and Alicia Ruvinsky. A fuzzy epigenetic model for representing degradation in engineered systems. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45582.

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Degradation processes are implicated in a large number of system failures, and are crucial to understanding issues related to reliability and safety. Systems typically degrade in response to stressors, such as physical or chemical environmental conditions, which can vary widely for identical units that are deployed in different places or for different uses. This situational variance makes it difficult to develop accurate physics-based or data-driven models to assess and predict the system health status of individual components. To address this issue, we propose a fuzzy set model for representing degradation in engineered systems that is based on a bioinspired concept from the field of epigenetics. Epigenetics is concerned with the regulation of gene expression resulting from environmental or other factors, such as toxicants or diet. One of the most studied epigenetic processes is methylation, which involves the attachment of methyl groups to genomic regulatory regions. Methylation of specific genes has been implicated in numerous chronic diseases, so provides an excellent analog to system degradation. We present a fuzzy set model for characterizing system degradation as a methylation process based on a set-theoretic representation for epigenetic modeling of engineered systems. This model allows us to capture the individual dynamic relationships among a system, environmental factors, and state of health.
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Robert S. Balch and Ronald F. Broadhead. A Customizable Fuzzy Expert System for Regional and Local Play Analysis. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/926645.

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7

Bechtel, James H. An Innovative Knowledge-Based System Using Fuzzy Cognitive Maps for Command and Control An Innovative Knowledge-Based System Using Fuzzy Cognitive Maps for Command and Control. Fort Belvoir, VA: Defense Technical Information Center, November 1997. http://dx.doi.org/10.21236/ada381723.

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House, William. Fuzzy Logic as a Tool to Compare Reliability of Torsion Bar System. Fort Belvoir, VA: Defense Technical Information Center, December 2009. http://dx.doi.org/10.21236/ada513266.

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9

Pan, Juiyao, Guilherme N. DeSouza, and Avinash C. Kak. FuzzyShell: A Large-Scale Expert System Shell Using Fuzzy Logic for Uncertainty Reasoning. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada335107.

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Chernichovsky, Dov. A Fuzzy Logic Approach Toward Solving the Analytic Maze of Health System Financing. Cambridge, MA: National Bureau of Economic Research, September 2001. http://dx.doi.org/10.3386/w8470.

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