Relevant bibliographies by topics / Neuro-fuzzy logic

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

Academic literature on the topic 'Neuro-fuzzy logic'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Neuro-fuzzy logic"

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Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.

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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks
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Titov, Andrei P. "SOFTWARE IMPLEMENTATION OF THE CO-ACTIVE NEURO-FUZZY INFERENCE SYSTEM." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 2 (2024): 26–43. http://dx.doi.org/10.28995/2686-679x-2024-2-26-43.

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The article deals with the implementation of a neural network with fuzzy logic based on the Co-Active Neuro-Fuzzy Inference System (CANFIS) model. The CANFIS model is an adaptive neuro-fuzzy system that combines neural networks and fuzzy logic for processing data with uncertainty and fuzziness. CANFIS uses fuzzy rules and output mechanisms to convert input data into output values. It consists of several layers, including an input layer, hidden layers and an output layer, where each layer contains neurons performing fuzzy activation and output of results. The relevance of the work lies in the f
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Katayama, Ryu. "Applications of Neuro Fuzzy Technology in Consumer Electronics Products." Journal of Robotics and Mechatronics 7, no. 1 (1995): 2–8. http://dx.doi.org/10.20965/jrm.1995.p0002.

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In recent years, intelligent industrial systems and consumer electronic products have been widely and intensively developed. Fuzzy logic, neural network, and neuro fuzzy technology, which integrates both approaches, are now regarded as an effective method to realize such intelligent features. In this paper, a review of the fuzzy boom in the consumer electronics market of Japan is presented. Typical applications of home appliances using fuzzy logic and neuro fuzzy technology are then described. Finally, methods and tools for developing fuzzy systems such as self-tuning and fuzzy modeling are re
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Chandrasekhar, Tadi, and Ch Sumanth Kumar. "Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System." Indian Journal Of Science And Technology 16, no. 13 (2023): 1014–20. http://dx.doi.org/10.17485/ijst/v16i13.1833.

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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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Jindal, Nikita, Jimmy Singla, Balwinder Kaur, et al. "Fuzzy Logic Systems for Diagnosis of Renal Cancer." Applied Sciences 10, no. 10 (2020): 3464. http://dx.doi.org/10.3390/app10103464.

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Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the curren
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Ashigwuike, Evans Chinemezu, and Stephen Adole Benson. "Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique." European Journal of Engineering Research and Science 4, no. 4 (2019): 83–89. http://dx.doi.org/10.24018/ejers.2019.4.4.1237.

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The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile
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Ashigwuike, Evans Chinemezu, and Stephen Adole Benson. "Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique." European Journal of Engineering and Technology Research 4, no. 4 (2019): 83–89. http://dx.doi.org/10.24018/ejeng.2019.4.4.1237.

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The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile
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Biswas, Saroj, Monali Bordoloi, and Biswajit Purkayastha. "Review on Feature Selection and Classification using Neuro-Fuzzy Approaches." International Journal of Applied Evolutionary Computation 7, no. 4 (2016): 28–44. http://dx.doi.org/10.4018/ijaec.2016100102.

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This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective
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Болгов, А. А. "RISK ASSESSMENT USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 4(-) (December 23, 2022): 521–30. http://dx.doi.org/10.36622/vstu.2022.25.4.006.

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В работе предлагается использование адаптивной нейро-нечеткой системы вывода для оценки риска. Проводится подробный обзор адаптивной нейро-нечеткой системы вывода, выделяя основные свойства этой системы в области методов оценки рисков. Приведены основные преимущества использования адаптивной нейро-нечеткой системы вывода. Рассматривается архитектура адаптивной нейро-нечеткой системы вывода. Выделены и рассмотрены основные методы обучения системы. Предложены методы оценки эффективности модели на основе адаптивной нейро-нечеткой системы вывода для оценки риска. Представлен алгоритм внедрения ада
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Dissertations / Theses on the topic "Neuro-fuzzy logic"

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Nukala, Ramesh Babu. "Neuro-fuzzy controllers for unstable systems." Thesis, Lancaster University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364362.

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Dalecký, Štěpán. "Neuro-fuzzy systémy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236066.

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The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system.  The thesis is aimed
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Thompson, Richard. "Neuro-fuzzy predictive control of an information-poor system." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:e463774c-a1c6-439e-a7e6-3cbb7aec68e3.

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While modern engineering systems have become increasingly integrated and complex over the years, interest in the application of control techniques which specifically attempt to formulate and solve the control problem in its inherently uncertain environment has been moderate, at best. More specifically, although many control schemes targeted at Heating, Ventilating and Air-Conditioning (HVAC) systems have been reported in the literature, most seem to rely on conventional techniques which assume that a detailed, precise model of the HVAC plant exists, and that the control objectives of the contr
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Silva, Aldo Antonio Vieira da [UNESP]. "Desenvolvimento de aplicações em medicina e agronomia utilizando lógica fuzzy e neuro fuzzy." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/110517.

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Made available in DSpace on 2014-11-10T11:09:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-02-28Bitstream added on 2014-11-10T11:58:00Z : No. of bitstreams: 1 000794270.pdf: 1454507 bytes, checksum: 21c1e569f66804233a47b876585652ce (MD5)<br>O presente trabalho propõe duas novas metodologias de desenvolvimento: uma na área de medicina, no diagnóstico de hérnia inguinal utilizando a lógica fuzzy e outra, na área da agronomia, para estimação da produção de trigo utilizando o modelo de inferência adaptativo neuro fuzzy. Na primeira foi desenvolvido um aplicativo para dispositivos móv
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Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.

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Raad, Raad. "Neuro-fuzzy admission control in mobile communications systems." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20061030.153500/index.html.

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Silva, Aldo Antonio Vieira da. "Desenvolvimento de aplicações em medicina e agronomia utilizando lógica fuzzy e neuro fuzzy /." Ilha Solteira, 2014. http://hdl.handle.net/11449/110517.

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Orientador: Marcelo Carvalho Minhoto Teixeira<br>Banca: Evaldo Assunção<br>Banca: Rodrigo Cardim<br>Banca: Cristiano Quevedo Andrea<br>Banca: Ruy de Oliveira<br>Resumo: O presente trabalho propõe duas novas metodologias de desenvolvimento: uma na área de medicina, no diagnóstico de hérnia inguinal utilizando a lógica fuzzy e outra, na área da agronomia, para estimação da produção de trigo utilizando o modelo de inferência adaptativo neuro fuzzy. Na primeira foi desenvolvido um aplicativo para dispositivos móveis, smartphones e tablets, auxiliando a tomada de decisão no diagnóstico de pacientes
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NETO, LUIZ SABINO RIBEIRO. "ARTIFICIAL NEURAL NETWORKS, FUZZY LOGIC AND NEURO-FUZZY SYSTEM IN THE ROLE OF SHORT TERM LOAD FORECAST." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7419@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>ELETROBRAS - CENTRAIS ELÉTRICAS BRASILEIRAS S. A.<br>Esta dissertação investiga o desempenho de técnicas de inteligência computacional na previsão de carga em curto prazo. O objetivo deste trabalho foi propor e avaliar sistemas de redes neurais, lógica nebulosa, neuro-fuzzy e híbridos para previsão de carga em curto prazo, utilizando como entradas variáveis que influenciam o comportamento da carga, tais como: temperatura, índice de conforto e perfil de consumo. Este trabalho envolve 4 etapas principais: um estudo sobre pr
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Silva, Inara Aparecida Ferrer [UNESP]. "Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/110516.

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Made available in DSpace on 2014-11-10T11:09:48Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-02-28Bitstream added on 2014-11-10T11:58:00Z : No. of bitstreams: 1 000794379.pdf: 1678626 bytes, checksum: b6d9b23c03cc8335775be33854ac5879 (MD5)<br>Os sistemas fuzzy e neuro fuzzy têm sido usados com sucesso para resolver problemas em diversas áreas, como medicina, indústria, controle, agronomia e aplicações acadêmicas. Nas últimas décadas, as redes neurais têm sido utilizadas para identificação, avaliação e previsão e dados na medicina e na agronomia. Nesta tese, realizou-se um novo estu
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Silva, Inara Aparecida Ferrer. "Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia /." Ilha Solteira, 2014. http://hdl.handle.net/11449/110516.

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Orientador: Marcelo Carvalho Minhoto Teixeira<br>Banca: Edvaldo Assunção<br>Banca: Aparecido Augusto de Carvalho<br>Banca: Cristiano Quevedo Andrea<br>Banca: Valtemir Emerencio do Nascimento<br>Resumo: Os sistemas fuzzy e neuro fuzzy têm sido usados com sucesso para resolver problemas em diversas áreas, como medicina, indústria, controle, agronomia e aplicações acadêmicas. Nas últimas décadas, as redes neurais têm sido utilizadas para identificação, avaliação e previsão e dados na medicina e na agronomia. Nesta tese, realizou-se um novo estudo comparativo entre as redes neuro fuzzy (ANFIS), re
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Books on the topic "Neuro-fuzzy logic"

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Horia-Nicolai, Teodorescu, Kandel Abraham, and Jain L. C, eds. Fuzzy and neuro-fuzzy systems in medicine. CRC Press, 1999.

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Roy, Samir. Introduction to soft computing: Neuro-fuzzy and genetic algorithms. Dorling Kindersley (India), 2013.

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Lee, R. S. T. Fuzzy-neuro approach to agent applications: From the AI perspective to modern ontology. Springer, 2006.

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Grima, Mario Alvarez. Neuro-fuzzy modeling in engineering geology: Applications to mechanical rock excavation, rock strength estimation, and geological mapping. A.A. Balkema, 2000.

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Fuzzy Logic and Neuro Fuzzy Applications Explained (Bk/Disk). Prentice Hall, 1995.

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Lee, Raymond S. T. Fuzzy-Neuro Approach to Agent Applications. Springer, 2005.

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Fuzzy and Neuro-Fuzzy Systems in Medicine. Taylor & Francis Group, 2017.

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Bothe, Hans-Heinrich. Neuro-Fuzzy-Methoden: Einführung in Theorie und Anwendungen. Springer, 1996.

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Ivancevic, Tijana T., and Vladimir G. G. Ivancevic. Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling. Springer, 2010.

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Ivancevic, Vladimir G., and Tijana T. Ivancevic. Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling. Springer London, Limited, 2007.

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Book chapters on the topic "Neuro-fuzzy logic"

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Brahim, Kais. "Neuro-Fuzzy Inferenz-Systeme." In Fuzzy Logic. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5_18.

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Schoder, Dagmar, and Hans Nücke. "Neuronale Netze und Fuzzy Logic in der Automatisierungstechnik." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_3.

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Teodorescu, H. N. L., and T. Yamakawa. "Neuro-fuzzy Systems: Hybrid Configurations." In Fuzzy Logic. Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_9.

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Bothe, Hans-Heinrich. "Einführung in die Fuzzy Logic." In Neuro-Fuzzy-Methoden. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58859-4_2.

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Reznik, Leonid. "Neuro-Fuzzy Control Applications: Looking for New Areas and Techniques?" In Fuzzy Logic. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1806-2_25.

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Maghooli, K., and A. M. Eftekhari Moghadam. "Development of Neuro-fuzzy System for Image Mining." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676935_4.

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Bede, Barnabas. "Artificial Neural Networks and Neuro-Fuzzy Systems." In Mathematics of Fuzzy Sets and Fuzzy Logic. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_14.

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Castellano, Giovanna, Anna Maria Fanelli, and Maria Alessandra Torsello. "A System for Deriving a Neuro-Fuzzy Recommendation Model." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02282-1_35.

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Azizan, Farah Liyana, Saratha Sathasivam, Majid Khan Majahar Ali, and Shehab Abdulhabib Saeed Alzaeemi. "Solving HornSAT Fuzzy Logic Neuro-symbolic Integration." In Studies in Systems, Decision and Control. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04028-3_5.

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Nagarajan, D., Khusbhu Chourashia, and A. Udhayakumar. "Neuro-Fuzzy Logic Application in Speech Recognition." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3611-3_1.

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Conference papers on the topic "Neuro-fuzzy logic"

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Körösi, Ladislav, Jana Paulusová, and Oliver Halaš. "Adaptive Neuro Fuzzy Inference System for Programmable Logic Controller." In 2025 Cybernetics & Informatics (K&I). IEEE, 2025. https://doi.org/10.1109/ki64036.2025.10916472.

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Amin, Ahmad Faishol, Ronny Cahyadi Utomo, and Khoirul Azis Rifa’i. "Comparing Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Auto-Cooling System in Generator Rotor Straightening." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747980.

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Kandukuri, Saritha, D. Ravi Kishore, Sunnapu Bhanu Prakash, Penna Ashok, and Koruprolu Jayadeep. "Neuro Fuzzy Logic Algorithm Based WECS-PMSG Interfaced Water pumping System Along with Battery-Energy Management System." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921814.

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B, Kavya Santhoshi, Ravi Kishore D, Nivesh Arja, V. V. Satya Sai Naga Geethika Baladari, and Chaitanya Kumar Guttula. "High Gain KY Converter For Grid Tied Clean Energy PV System Using Cascaded Neuro Fuzzy Logic MPPT Algorithm." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921792.

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Srichiangsa, Theeraphong, Piyapath Siratarnsophon, Sirichai Wattanasophon, and Sarinee Ouitrakul. "Comparative Analysis of PID, Self-Tunning PID, and Adaptive Neuro-Fuzzy Logic Inference System Controllers for BLDC Motor Speed Control." In 2024 27th International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2024. https://doi.org/10.23919/icems60997.2024.10921276.

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Tayal, Shikha, Taskeen Zaidi, and Preeti Gera. "Utilization of Support Vector Machines (SVM), Fuzzy Logic (FL) & Adaptive Neuro-Fuzzy Inference System (ANFIS) for Carrying Out Proficient Energy Routing in 5G Wireless Networks." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866528.

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Allur, Naga Sushma, Koteswararao Dondapati, Himabindu Chetlapalli, Sharadha Kodadi, Durga Praveen Deevi, and Purandhar N. "Robotic Automation Dynamic Hybrid Neuro-Fuzzy and Deep Learning Framework with GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic and CNN-TCN for Accurate IoMT-Based Chronic Kidney Disease Detection." In 2025 International Conference on Computer, Electrical & Communication Engineering (ICCECE). IEEE, 2025. https://doi.org/10.1109/iccece61355.2025.10940081.

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Misra, R. B. "Tamper detection using neuro-fuzzy logic." In Ninth International Conference on Metering and Tariffs for Energy Supply. IEE, 1999. http://dx.doi.org/10.1049/cp:19990115.

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Ruprecht, Blake, Wenlong Wu, Muhammad Aminul Islam, et al. "Possibilistic Clustering Enabled Neuro Fuzzy Logic." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177593.

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Ali Adem, Mohammed. "Energy Optimization of Wireless Sensor Network Using Neuro-Fuzzy Algorithms." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120814.

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Wireless sensor network (WSN) is one of the recent technologies in communication and engineering world to assist various civilian and military applications. They are deployed remotely in sever environment which doesn’t have an infrastructure. Energy is a limited resource that needs efficient management to work without any failure. Energy efficient clustering of WSN is the ultimate mechanism to conserve energy for longtime. The major objective of this research is to efficiently consume energy based on the Neuro-Fuzzy approach particularly adaptive Neuro fuzzy inference system (ANFIS). The signi
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Reports on the topic "Neuro-fuzzy logic"

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Lavrentieva, Olena O., Ihor O. Arkhypov, Olexander I. Kuchma, and Aleksandr D. Uchitel. Use of simulators together with virtual and augmented reality in the system of welders’ vocational training: past, present, and future. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3748.

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The article discusses the theory and methods of simulation training, its significance in the context of training specialists for areas where the lack of primary qualification is critical. The most widespread hardware and software solutions for the organization welders' simulation training that use VR- and AR- technologies have been analyzed. A review of the technological infrastructure and software tools for the virtual teaching-and-production laboratory of electric welding has been made on the example of the achievements of Fronius, MIMBUS, Seabery. The features of creating a virtual simulati
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