Relevant bibliographies by topics / Fuzzy inference system expert

Contents

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

Academic literature on the topic 'Fuzzy inference system expert'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Fuzzy inference system expert"

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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 (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 b
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Istiadi, Istiadi, Emma Budi Sulistiarini, Rudy Joegijantoro, Anik Vega Vitianingsih, and Affi Nizar Suksmawati. "Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious Diseases." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 6 (2022): 1047–56. http://dx.doi.org/10.29207/resti.v6i6.4656.

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E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective, so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the i
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Manek, Siprianus Septian, Grandianus Seda Mada, and Yoseph P. K. Kelen. "PRE-ECLAMPSIA DIAGNOSIS EXPERT SYSTEM USING FUZZY INFERENCE SYSTEM MAMDANI." Jurnal Techno Nusa Mandiri 20, no. 2 (2023): 80–88. http://dx.doi.org/10.33480/techno.v20i2.4622.

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Various institutions utilize computer information systems to analyze and process data. An expert system is an information system that is used to help analyze and determine decisions on a problem based on rules determined by experts. This research focuses on creating a prototype expert system for diagnosing pre-eclampsia or pregnancy poisoning in pregnant women based on measuring blood pressure and checking proteinuria. The existing data is then analyzed using the Mamdani system's fuzzy inference method. Supporting theory regarding the fuzzy inference system of Mamdani, pre-eclampsia and its ex
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LEE, KEON-MYUNG, and HYUNG LEE-KWANG. "FUZZY INFORMATION PROCESSING FOR EXPERT SYSTEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 01 (1995): 93–109. http://dx.doi.org/10.1142/s0218488595000098.

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This paper investigates the problems incurred when fuzzy values and certainty factors are used in rule-based knowledge representation. It proposes several measures for evaluating the satisfaction degree of fuzzy matching, fuzzy comparison and interval inclusion occurring in the course of inference for such knowledge representation. It introduces an inference method for such knowledge representation. In addition, it suggests a strategy for flexibly using and managing both conventional rules and fuzzy production rules in rule-based systems. Finally a fuzzy expert system shell, called FOPS5, desi
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Latafat Gardashova, Aytaj Ismayilova, Latafat Gardashova, Aytaj Ismayilova, and Gulay Tarverdiyeva Gulay Tarverdiyeva. "EXPERT SYSTEM FOR DENTAL DISEASES." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 31, no. 08 (2023): 23–30. http://dx.doi.org/10.36962/pahtei31082023-23.

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The objective of an expert system in the field of medicine is to support doctors during the diagnosis process. Any software that is capable of drawing conclusions and making decisions based on the data stored in its database can be called an "expert system." Expert systems are widely employed in many industries, including the health sector. There are numerous types of dental ailments in the field of dentistry. Few symptoms were employed in the existing methods for dental diagnostics. A diagnosis in dentistry requires more than a few symptoms. The aim of this chapter is to analyse a medical exp
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Chang, Chung-Liang, and Ming-Fong Sie. "A Multistaged Fuzzy Logic Scheme in a Biobotanic Growth Regulation System." HortScience 47, no. 6 (2012): 762–70. http://dx.doi.org/10.21273/hortsci.47.6.762.

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A multistaged fuzzy logic control method was used in the development of a bionic botanical growth control system. The growth mode combined fuzzy logic inference with expert knowledge to regulate the growth rate of plants. First, environment factors such as the light, temperature, and water required for plants in different stages of growth were analyzed. Fuzzy logic was then used to establish membership functions, an inference engine, and rule table. An expert database related to plant growth was combined with the fuzzy logic controller to formulate a plant growth control system. Sunflowers wer
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Ganzhur, Marina, Alexey Ganzhur, Nikita Dyachenko, Andrey Kobylko, and Alexander Melnikov. "Data analysis using system modeling." E3S Web of Conferences 389 (2023): 07005. http://dx.doi.org/10.1051/e3sconf/202338907005.

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Expert systems are increasingly being used to format safe operations. But the functions of expert systems can perform not only assistance in making decisions, but also analyze processes and help at various stages. These actions are possible when considering a system with fuzzy data. The work is devoted to solving the problem of fuzzy inference knowledge in intelligent systems based on the use of fuzzy logic. The scheme of construction of continuous logic, the computation of values of membership functions of linguistic variables of output knowledge. The proposed approach is based on the use of
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Hasanah, Nur, and Retantyo Wardoyo. "Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 7, no. 1 (2013): 45. http://dx.doi.org/10.22146/ijccs.3051.

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AbstrakPada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakur
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Hirota, Kaoru, MingQiang Xu, Yasufumi Takama, and Hajime Yoshino. "Implementation of Fuzzy Legal Expert System FLES." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 6 (2000): 421–27. http://dx.doi.org/10.20965/jaciii.2000.p0421.

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A fuzzy legal expert system FLES based on a fuzzy Housdorff similarity measure is implemented. The reasoning approach in this system includes the fuzzy case-based reasoning that is composed of knowledge representation, retrieval, and inference. The proposed approaches are illustrated by the experiments, where the target law is CISG (United Nation Convention on Contract for the International Sale of Goods).
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PANIAGUA MEDINA, JUAN JOSE, Sarahí Camargo Carmona, ANA DINORA GUZMAN CHAVEZ, and Everardo Vargas Rodríguez. "FUZZY INFERENCE SYSTEM FOR DIAGNOSING STRESS AND ITS EVOLUTION IN LAYING HENS." DYNA NEW TECHNOLOGIES 10, no. 1 (2023): [11P.]. http://dx.doi.org/10.6036/nt10901.

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In this work a fuzzy inference system design to estimate if laying hens present some level of stress without an expert intervention, as a poultry veterinarian, is presented. Additionally, in small farms usually hens diagnosed as stressed are isolated during some days until they are recovered. Moreover, isolated hens are diary examined by the expert to diagnose if the stress has disappeared. Here, it is important to point out that experts usually are unable to estimate the number of days that the hen will need to be kept in isolation until they are recovered. Therefore, as an additional advanta
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Dissertations / Theses on the topic "Fuzzy inference system expert"

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Martin-Clouaire, Roger. "Representation et utilisation de meta-connaissances et d'informations imprecises ou incertaines." Toulouse 3, 1986. http://www.theses.fr/1986TOU30246.

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Dans l'approche developpee, les meta connaissances sont exprimees declarativement via des metaregles ayant pour role de: suggerer comment utiliser les connaissances de base; orienter le systeme sur les donnees adequates; permettre de hierarchiser les etapes du raisonnement et restructurer les donnees suivant des considerations spatiales. Quant au moteur d'inference spii (systeme de propagation de l'imprecision et de l'incertitude), il permet un traitement homogene, dans le cadre de la theorie des possibilites, des informations (regles ou faits) tant imprecises qu'incertaines
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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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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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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 t
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Bodapatti, Nageswararao. "Fuzzy-expert system for voltage stability monitoring and control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0010/MQ36098.pdf.

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Guner, Evren. "Adaptive Neuro Fuzzy Inference System Applications In Chemical Processes." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1252246/index.pdf.

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Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-output data pairs. Effective control for distillation systems, which are one of the importa
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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases
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Sun, Zhuo. "A fuzzy expert system for design performance prediction and evaluation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0010/MQ60182.pdf.

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Muthu, Kavitha. "Expert system and fuzzy technique approaches to landslide hazard mapping." Thesis, University of Surrey, 2005. http://epubs.surrey.ac.uk/722/.

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Singh, Aditya Kumar. "Design and development of fuzzy expert system for handy board." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=1177.

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Thesis (M.S.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains v, 134 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 66-69).
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Books on the topic "Fuzzy inference system expert"

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

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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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Moti, Schneider, ed. Fuzzy expert system tools. John Wiley, 1996.

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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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M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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B, Sheridan Thomas, and Lyndon B. Johnson Space Center. Information Technology Division., eds. Expert system training and control based on the fuzzy relation matrix: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1991.

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

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Chernichovsky, Dov. A fuzzy logic approach toward solving the analytic maze of health system financing. National Bureau of Economic Research, 2001.

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Tamir, Dan E. A schema for knowledge representation and its implementation in a computer-aided design and manufacturing system. TÜV Rheinland, 1989.

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Book chapters on the topic "Fuzzy inference system expert"

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Cornez, Laurence, Manuel Samuelides, and Jean-Denis Muller. "Neuro-Fuzzy Inference System to Learn Expert Decision: Between Performance and Intelligibility." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_168.

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Chainani, Ajay P., Santosh S. Chikne, Nikunj D. Doshi, Asim Z. Karel, and Shanthi S. Therese. "Disease Inference from Health-Related Questions via Fuzzy Expert System." In Information and Communication Technology for Sustainable Development. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3920-1_10.

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Pathak, Dharmendra, and Mohit Arora. "Adaptive Neuro-Fuzzy Inference Expert System for Agile-Inspired Software Development." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-716-8_36.

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Aiello, Giuseppe, Antonella Certa, and Mario Enea. "A Fuzzy Inference Expert System to Support the Decision of Deploying a Military Naval Unit to a Mission." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02282-1_40.

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Zhang, Zipeng, Shuqing Wang, and Xiaohui Yuan. "Advanced Self-adaptation Learning and Inference Techniques for Fuzzy Petri Net Expert System Units." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05253-8_54.

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Biswas, Animesh, Debasish Majumder, and Subhasis Sahu. "Assessing Morningness of a Group of People by Using Fuzzy Expert System and Adaptive Neuro Fuzzy Inference Model." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19263-0_6.

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Soni, Rahul, and Bhinal Mehta. "Condition-Based Monitoring of Power Transformer with Graphical Analysis of Incipient Faults Using Fuzzy Inference Expert System." In Advances in Data Science and Computing Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3656-4_34.

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Sivaram, M., Amin Salih Mohammed, D. Yuvaraj, V. Porkodi, V. Manikandan, and N. Yuvaraj. "Advanced Expert System Using Particle Swarm Optimization Based Adaptive Network Based Fuzzy Inference System to Diagnose the Physical Constitution of Human Body." In Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8300-7_29.

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Padilla, Cynthia Cristina Martinez. "Rule-Based Expert System with Bayesian Theory and Fuzzy Inference for Vocational Guidance: A Tool to Prevent School Dropouts." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83879-8_12.

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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. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15585-8_4.

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Conference papers on the topic "Fuzzy inference system expert"

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Manimala, R., and B. Vigneshwaran. "Comparison of Mamdani and Sugeno Fuzzy Inference Systems to Assess the Health Condition of Power Transformer." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00096.

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Huang, Linying. "Vocal Performance Evaluation System Based on Fuzzy Inference System." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10968448.

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Hereford, James M. "Optical expert system with fuzzy response capability." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thw19.

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A rule based expert system is described that distinguishes targets from background clutter. The expert system uses a Bayesian inference system and has the unique feature of allowing graduated or fuzzy responses for the different “events.” This allows the system to handle non-exact or uncertain data from the input seeker. For instance, one can specify (on a scale of –1 to 1) if something is “pretty large” or “relatively flat.” The optical implementation is based on the diagnostic expert system design described by McAulay.1 It uses a 1-D SLM to handle the input (fuzzy) responses and a 2-D SLM to
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Dhami, S. S., S. S. Bhasin, and P. B. Mahapatra. "Design of a Fuzzy Logic Controller Using ANFIS for Accurate Position Control of a Pneumatic Servo System." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66940.

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A methodology for designing a Sugeno type Fuzzy Logic Controller (FLC) for accurate position control of a pneumatic servo system is presented. Adaptive Neuro Fuzzy Inference System technique is employed to construct a fuzzy inference system whose membership function parameters are tuned using a training data set comprising of input/output signal of the pneumatic servo system with proportional control. Hybrid backpropogation-least square algorithm is used for training of the Fuzzy Inference System (FIS). The resulting FIS optimally projected the behavior of training data set. To obtain the desi
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Zhou Ming. "A distributed power system fault diagnosis expert system based on fuzzy inference." In APSCOM 2000 - 5th International Conference on Advances in Power System Control, Operation and Management. IEE, 2000. http://dx.doi.org/10.1049/cp:20000404.

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Poli, Venkata Subba Reddy. "Method of fuzzy conditional inference and application to fuzzy medical expert systems." In 2015 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2015. http://dx.doi.org/10.1109/ifuzzy.2015.7391904.

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Silva, Francisco, Brigida Teixeira, Nuno Teixeira, Tiago Pinto, Isabel Praca, and Zita Vale. "Application of a Hybrid Neural Fuzzy Inference System to Forecast Solar Intensity." In 2016 27th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2016. http://dx.doi.org/10.1109/dexa.2016.044.

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Pamplona Filho, Cesar Roberto, Maira Junkes Cunha, Fernando Mendes de Azevedo, and Giselle Lopes Ferrari. "Intellec System: Shell for expert systems creation with fuzzy inference machine developed in prolog." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551819.

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Guillaume, Serge, and Brigitte Charnomordic. "Interpretable fuzzy inference systems for cooperation of expert knowledge and data in agricultural applications using FisPro." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584673.

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Hong, Guang, Xin Chen, Xuedong Xue, and Shuai Zhang. "Expert Systems for Fault Diagnosis Integrating Neural Network and Fuzzy Inference." In 2011 International Conference on Information Technology, Computer Engineering and Management Sciences (ICM). IEEE, 2011. http://dx.doi.org/10.1109/icm.2011.170.

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Reports on the topic "Fuzzy inference system expert"

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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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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), 2007. http://dx.doi.org/10.2172/926645.

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

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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.], 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 knowl
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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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Karam, Sofia, Morteza Nagahi, Vidanelage Dayarathna, Junfeng Ma, Raed Jaradat, and Michael Hamilton. Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41026.

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The emergence of modern complex systems is often exacerbated by a proliferation of information and complication of technologies. Because current complex systems challenges can limit an organization's ability to efficiently handle socio-technical systems, it is essential to provide methods and techniques that count on individuals' systems skills. When selecting future employees, companies must constantly refresh their recruitment methods in order to find capable candidates with the required level of systemic skills who are better fit for their organization's requirements and objectives. The pur
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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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