Relevant bibliographies by topics / Neuro-fuzzy

Contents

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

Academic literature on the topic 'Neuro-fuzzy'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Neuro-fuzzy"

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Kişi, Özgür, and Turgay Partal. "Wavelet and neuro-fuzzy conjunction model for streamflow forecasting." Hydrology Research 42, no. 6 (2011): 447–56. http://dx.doi.org/10.2166/nh.2011.048.

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In this study the wavelet-neuro-fuzzy model, which combines the wavelet transform and the neuro-fuzzy technique, has been employed to forecast monthly streamflows. The observed monthly streamflow data are decomposed into some sub-series (components) by discrete wavelet transform and then appropriate sub-series are used as inputs to the neuro-fuzzy models for forecasting monthly streamflows. The data from two stations, Durucasu and Tanir, in Turkey are used as case studies. The wavelet-neuro-fuzzy forecasts are compared with those of the single neuro-fuzzy models. Comparison results indicate th
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Ломакина, Л. С., and И. Д. Чернобаев. "Neuro-fuzzy classifiers." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 9, no. 4(35) (2021): 27–28. http://dx.doi.org/10.26102/2310-6018/2021.35.4.027.

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В статье рассматривается проблема повышения точности искусственных нейронных сетей при решении задач классификации состояний объектов различной физической природы. Эту проблему предлагается сформулировать как проблему выбора типа функции активации в искусственных нейронных сетях и рассматривать ее с позиции теории нечетких множеств. В этой связи разработана математическая модель адаптивной функции активации искусственного нейрона, использующая нечеткую логическую систему с интервальными нечеткими множествами второго типа. Эта функция отличается от обыкновенных функций активации, применяемых в
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Kisi, Ozgur, and Jalal Shiri. "Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations." Hydrology Research 43, no. 3 (2012): 286–300. http://dx.doi.org/10.2166/nh.2012.104b.

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The ability of a wavelet and neuro-fuzzy conjunction technique for groundwater depth forecasting was investigated in this study. The wavelet-neuro-fuzzy model was improved by combining two methods, the discrete wavelet transform and the neuro-fuzzy model. The conjunction model was applied to different input combinations of daily groundwater depth data of Bondville and Perry wells. Root mean square error (RMSE) and correlation coefficient (R) statistics were used for evaluating the accuracy of wavelet-neuro-fuzzy models. The accuracy of the conjunction models was compared with those of the sing
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Nowicki, Robert. "On classification with missing data using rough-neuro-fuzzy systems." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 55–67. http://dx.doi.org/10.2478/v10006-010-0004-8.

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On classification with missing data using rough-neuro-fuzzy systemsThe paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.
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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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VAIRAPPAN, CATHERINE, SHANGCE GAO, ZHENG TANG, and HIROKI TAMURA. "ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS." International Journal of Computational Intelligence and Applications 08, no. 04 (2009): 429–44. http://dx.doi.org/10.1142/s1469026809002680.

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A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy fee
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Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 177–85. http://dx.doi.org/10.20965/jaciii.1999.p0177.

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Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adap
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Astuti, Winda Try, Much Aziz Muslim, and Endang Sugiharti. "The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas." Scientific Journal of Informatics 6, no. 1 (2019): 95–105. http://dx.doi.org/10.15294/sji.v6i1.16648.

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The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of
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Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and labora
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Simiński, Krzysztof. "Neuro-rough-fuzzy approach for regression modelling from missing data." International Journal of Applied Mathematics and Computer Science 22, no. 2 (2012): 461–76. http://dx.doi.org/10.2478/v10006-012-0035-4.

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Neuro-rough-fuzzy approach for regression modelling from missing dataReal life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results
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Dissertations / Theses on the topic "Neuro-fuzzy"

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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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SOUZA, FLAVIO JOAQUIM DE. "HIERARCHICAL NEURO-FUZZY MODELS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7576@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação apresenta uma nova proposta de sistemas (modelos) neuro-fuzzy que possuem, além do tradicional aprendizado dos parâmetros, comuns às redes neurais e aos sistemas nero-fuzzy, as seguintes características: aprendizado de estrutura, a partir do uso de particionamentos recursisvos; número maior de entradas que o comumente encontrado nos sistemas neuro-fuzzy; e regras com hierarquia. A definição da estrutura é uma necessidade que surge quando da implementação de um determinado modelo. Pode-se citar o caso das
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Palancioglu, Haci Mustafa. "Extracting Movement Patterns Using Fuzzy and Neuro-fuzzy Approaches." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/PalanciogluHM2003.pdf.

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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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Po-ngaen, Watcharin. "Neuro-fuzzy control in tele-robotics." Thesis, University of Newcastle Upon Tyne, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430351.

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BEZERRA, ROSINI ANTONIO MONTEIRO. "HIERARCHICAL NEURO-FUZZY BSP-MAMDANI MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3129@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação investiga a utilização de sistemas Neuro- Fuzzy Hierárquicos BSP (Binary Space Partitioning) para aplicações em classificação de padrões, previsão, sistemas de controle e extração de regras fuzzy. O objetivo é criar um modelo Neuro-Fuzzy Hierárquico BSP do tipo Mamdani a partir do modelo Neuro-Fuzzy Hierárquico BSP Class (NFHB-Class) que é capaz de criar a sua própria estrutura automaticamente e extrair conhecimento de uma base de dados através de regras fuzzy, lingüisticamente interpretáveis, que explicam
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Khiyo, Sargon, University of Western Sydney, of Science Technology and Environment College, and School of Engineering and Industrial Design. "Neuro/fuzzy speed control of induction motors." THESIS_CSTE_EID_Khiyo_S.xml, 2002. http://handle.uws.edu.au:8081/1959.7/554.

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The thesis involved the design, implementation and testing of a second order neuro-fuzzy controller for the speed control of an AC induction motor, and a comparison of the neuro-fuzzy controller's performance with that of the PI algorithm. It was found experimentally, that the operating temperature of the AC induction motor affected the ability of the PI controller to maintain the set speed. The linear PI algorithm approximation was observed to produce transient speed responses when sudden changes in load occurred. The neuro-fuzzy design was found to be quite involved in the initial design sta
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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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Prajitno, Prawito. "Neuro-fuzzy methods in multisensor data fusion." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251258.

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CONTRERAS, ROXANA JIMENEZ. "TYPE-2 HIERARCHICAL NEURO-FUZZY BSP MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10862@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho tem por objetivo criar um novo sistema de inferência fuzzy intervalar do tipo 2 para tratamento de incertezas com aprendizado automático e que proporcione um intervalo de confiança para as suas saídas defuzzificadas através do cálculo dos conjuntos tipo-reduzidos correspondentes. Para viabilizar este objetivo, este novo modelo combina os paradigmas de modelagem dos sistemas de inferência fuzzy do tipo 2 e redes neurais com técnicas de particionamento recursivo BSP. Este modelo possui principalmente a capacidad
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Books on the topic "Neuro-fuzzy"

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Zimmermann, Hans-Jürgen, ed. Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3.

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Bothe, Hans-Heinrich. Neuro-Fuzzy-Methoden. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58859-4.

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Borgelt, Christian, Frank Klawonn, Rudolf Kruse, and Detlef Nauck. Neuro-Fuzzy-Systeme. Vieweg+Teubner Verlag, 2003. http://dx.doi.org/10.1007/978-3-322-80336-8.

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Czogała, Ernest, and Jacek Łęski. Fuzzy and Neuro-Fuzzy Intelligent Systems. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1853-6.

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Horst, Bunke, and Kandel Abraham, eds. Neuro-fuzzy pattern recognition. World Scientific, 2000.

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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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Stavroulakis, Peter, ed. Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18762-9.

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Stavroulakis, Peter. Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications. Springer Berlin Heidelberg, 2004.

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Fullér, Robert. Introduction to Neuro-Fuzzy Systems. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9.

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Rudolf, Kruse, and Klawonn F, eds. Foundations of neuro-fuzzy systems. John Wiley, 1997.

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Book chapters on the topic "Neuro-fuzzy"

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Zimmermann, Hans-Jürgen. "Industrielle Einsatzgebiete von Fuzzy Technologien." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_1.

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Fischer, Bernd, Willi Meier, and Hans-Jörg Minas. "Pensum — Ein Fuzzy Planungs- und Steuerungssystem für die operative Prozeßführung." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_10.

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Zimmermann, Hans-Jürgen. "Ergänzende Literatur." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_11.

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Flaton, Kenneth A., Stefan Gehlen, Michael Hormel, Wolfgang Konen, and Jörg Kopecz. "Neuronale Netze: Grundlagen und Anwendungen." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_2.

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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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Neumerkel, Dietmar. "Neuronale Regelung für industrielle Prozesse." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_4.

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Martinetz, Thomas. "Lernverfahren für komplexe Aktorik / Robotik." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_5.

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Froese, Thomas. "Übergeordnete Regelung chemischer Verfahren mit Fuzzy Control und Künstlichen Neuronalen Netzen." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_6.

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Furomoto, Herbert. "Einsatz von Fuzzy Control und neuronalen Netzen bei der Automatisierung in der Zellstoff- und Papierindustrie." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_7.

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Lieven, Karl, Willi Meier, Richard Weber, and Hans Jürgen Zimmermann. "Methoden und Anwendungen der Fuzzy Datenanalyse und Neuro-Fuzzy Systeme." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_8.

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Conference papers on the topic "Neuro-fuzzy"

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Li, Chunshien, and Zhao-Yi Tsai. "Hardware Neuro-Fuzzy Learning." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.413.

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Bhogle, P. P., B. M. Patre, L. M. Waghmare, and V. M. Panchade. "Neuro Fuzzy Temperature Controller." In 2007 International Conference on Mechatronics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icma.2007.4304099.

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Fritzke, Bernd. "Incremental neuro-fuzzy systems." In Optical Science, Engineering and Instrumentation '97, edited by Bruno Bosacchi, James C. Bezdek, and David B. Fogel. SPIE, 1997. http://dx.doi.org/10.1117/12.284208.

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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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Sagi, Andor, Anita Sabo, Bojan Kuljic, and Tibor Szakall. "Neuro-fuzzy systems in medicine." In 2010 11th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2010. http://dx.doi.org/10.1109/cinti.2010.5672229.

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Viharos, Zsolt Janos, and Krisztian Balazs Kis. "Optimal Neuro-Fuzzy model configuration." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2016. http://dx.doi.org/10.1109/smc.2016.7844799.

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Nabil, Mohamed Sherif, Mohamed Misbah ElKhatib, and Ashraf Tammam. "Neuro Fuzzy DC-DC Converter." In 2018 28th International Conference on Computer Theory and Applications (ICCTA). IEEE, 2018. http://dx.doi.org/10.1109/iccta45985.2018.9499194.

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Tan Ai, Richard Josiah C., and Elmer P. Dadios. "Neuro-Fuzzy Mobile Robot Navigation." In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM). IEEE, 2018. http://dx.doi.org/10.1109/hnicem.2018.8666348.

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Damiani, Ernesto, and Athanasios Vasilakos. "Session details: Neuro-fuzzy applications." In SAC02: 2002 ACM Symposium on Applied Computing. ACM, 2002. http://dx.doi.org/10.1145/3253209.

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Lee, Sang Yeal, Young Jun Park, and Hyung Suck Cho. "A Neuro-Fuzzy Control of an Electro-Hydraulic Fin Position Servo System." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0549.

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Abstract The electro-hydraulic servo systems have been frequently used in the fin position servo system of a missile because of their high power and good positioning capabilities. This paper presents a neuro-fuzzy control scheme for an electro-hydraulic fin position servo system. In this paper, we have explained a fuzzy control scheme which has multidimensional and asymmetrical membership functions, and we have designed a neuro-fuzzy controller which include parameters of the fuzzy logic controller. The effectiveness of this control scheme is verified by comparison with the conventional contro
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Reports on the topic "Neuro-fuzzy"

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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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Esfahani, Naser Madani, Behzad Heidari, Hamed Boustanzar, and Ghasem Sattari. Modeling of Uniaxial Compressive Strength via Genetic Programming and Neuro-Fuzzy. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0304.

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Thangaraj, Rammohan, Sathish Kumar Shanmugam, Sivachitra Muthusamy, Radhamani Vijayakumar, Asha Aiyappan, and Hariprabhu Manoharan. Power Quality Improvement Based on Shunt Active Filter for Harmonic Suppression Using Neuro-Fuzzy. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.08.12.

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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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You might also be interested in the extended bibliographies on the topic 'Neuro-fuzzy' for particular source types:
Journal articles Dissertations / Theses Books
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