Relevant bibliographies by topics / Neural fuzzy network

Academic literature on the topic 'Neural fuzzy network'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Neural fuzzy network"

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Kruse, Rudolf. "Fuzzy neural network." Scholarpedia 3, no. 11 (2008): 6043. http://dx.doi.org/10.4249/scholarpedia.6043.

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Zamirpour, Ehsan, and Mohammad Mosleh. "A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network." Biologically Inspired Cognitive Architectures 26 (October 2018): 80–90. http://dx.doi.org/10.1016/j.bica.2018.07.019.

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Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (December 27, 2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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Zhang, Yong Chao, Wen Zhuang Zhao, and Jin Lian Chen. "The Research and Application of the Fuzzy Neural Network Control Based on Genetic Algorithm." Advanced Materials Research 403-408 (November 2011): 191–95. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.191.

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How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network intelligent control system.
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Nishina, Takatoshi, and Masafumi Hagiwara. "Fuzzy inference neural network." Neurocomputing 14, no. 3 (February 1997): 223–39. http://dx.doi.org/10.1016/s0925-2312(96)00036-7.

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Ciaramella, A., R. Tagliaferri, W. Pedrycz, and A. Di Nola. "Fuzzy relational neural network." International Journal of Approximate Reasoning 41, no. 2 (February 2006): 146–63. http://dx.doi.org/10.1016/j.ijar.2005.06.016.

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Li, Ye, Xiao Liu, Zhenliang Yang, Chao Zhang, Mingchun Song, Zhaolu Zhang, Shiyong Li, and Weiqiang Zhang. "Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic." Scientific Programming 2022 (March 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/2630953.

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The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.
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Shi, De Quan, Gui Li Gao, Ying Liu, Hui Ying Tang, and Zhi Gao. "Temperature Controller of Heating Furnace Based on Fuzzy Neural Network Technology." Advanced Materials Research 748 (August 2013): 820–25. http://dx.doi.org/10.4028/www.scientific.net/amr.748.820.

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In this study, to solve the problem that heating furnace has the disadvantage of non-linearity, time variant and large delay, a fuzzy neural network controller has been designed according to the combination of fuzzy control and neural networks. In this controller, not only can the reasoning process of neural network be described by the fuzzy rules, but also the fuzzy rules can be dynamically adjusted by the neural network. In addition, the learning algorithm of the fuzzy neural network controller is studied. Simulation results show that the fuzzy neural network controller has good regulating performance and it can meet the needs of heating furnace during industrial production.
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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 (September 30, 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 and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.
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Teja, G. Ravi, and M. R. Narasinga Rao. "Image Retrieval System using Fuzzy-Softmax MLP Neural Network." Indian Journal of Applied Research 3, no. 6 (October 1, 2011): 169–74. http://dx.doi.org/10.15373/2249555x/june2013/57.

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Dissertations / Theses on the topic "Neural fuzzy network"

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Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.

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The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most appropriate route for transmitting data. Determining the best route through a wide area network (WAN), requires the routing algorithm to obtain information concerning all of the nodes, links, and devices present on the network. The most relevant routing information involves various measures that are often obtained in an imprecise or inaccurate manner, thus suggesting that fuzzy reasoning is a natural method to employ in an improved routing scheme. The neural network is deemed as a suitable accompaniment because it maintains the ability to learn in dynamic situations. Once the neural network is initially designed, any alterations in the computer routing environment can easily be learned by this adaptive artificial intelligence method. The capability to learn and adapt is essential in today's rapidly growing and changing computer networks. These techniques, fuzzy reasoning and neural networks, when combined together provide a very effective routing algorithm for computer networks. Computer simulation is employed to prove the new fuzzy routing algorithm outperforms the Shortest Path First (SPF) algorithm in most computer network situations. The benefits increase as the computer network migrates from a stable network to a more variable one. The advantages of applying this fuzzy routing algorithm are apparent when considering the dynamic nature of modern computer networks.
Ph. D.
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James, Keith. "Online adaptive fuzzy neural network automotive engine control." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/9089.

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Automotive manufacturers are investing in research and development for hybridization and more modern advanced combustion strategies. These new powertrain systems can offer the higher efficiency required to meet future emission legislation, but come at the cost of significantly increased complexity. The addition of new systems to modernise an engine increases the degrees of freedom of the control problem and the number of control variables. Advanced combustion strategies also display interlinked behaviour between control variables. This type of behaviour requires a more orchestrated multi-input multi-output control approach. Model based control is a common solution, but accurate control models can be difficult to achieve and calibrate due to the nonlinear dynamics of the engines. The modelling problem becomes worse when some advanced combustion systems display nonlinear dynamics that can change with time. Any fixed model control system would suffer from increasing model/system mismatch. Direct feedback would help reduce a degree or error from model/system mismatch, but feedback methods are often limited by cost and are generally indirect and slow response. This research addresses these problems with the development of a mobile ionisation sensor and an online adaptive control architecture for multi-input multi-output engine control. The mobile ionisation system offers a cheap, fast response, direct in-cylinder feedback for combustion control. Feedback from 30 averaged cycles can be related to combustion timing with variance as small as 0.275 crank angle degrees. The control architecture combines neural networks and fuzzy logic for the control and reduced modelling effort for complex nonlinear systems. The combined control architecture allows continuous online control adaption for calibration against model/plant mismatch and time varying dynamics. In simulation, set point tracking could be maintained for combustion timing to 4 CAD and AFR to 4, for significant dynamics shifts in plant dynamics during a transient drive cycle.
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Gabrys, Bogdan. "Neural network based decision support : modelling and simulation of water distribution networks." Thesis, Nottingham Trent University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387534.

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Wang, Ziqing. "Fuzzy neural network for edge detection and Hopfield network for edge enhancement." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0005/MQ42458.pdf.

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Hudgins, Billy E. "Implementation of fuzzy inference systems using neural network techniques." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23919.

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Campbell, Jonathan G. "Fuzzy logic and neural network techniques in data analysis." Thesis, University of Ulster, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342530.

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Ara?jo, J?nior Jos? Medeiros de. "Identifica??o n?o linear usando uma rede fuzzy wavelet neural network modificada." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15249.

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Made available in DSpace on 2014-12-17T14:55:19Z (GMT). No. of bitstreams: 1 JoseMAJ_TESE.pdf: 3560157 bytes, checksum: 2f20316c7b980a74bdb7b82e97e3bb43 (MD5) Previous issue date: 2014-03-24
Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
In last decades, neural networks have been established as a major tool for the identification of nonlinear systems. Among the various types of networks used in identification, one that can be highlighted is the wavelet neural network (WNN). This network combines the characteristics of wavelet multiresolution theory with learning ability and generalization of neural networks usually, providing more accurate models than those ones obtained by traditional networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks, with the difference that traditional polynomials present in consequent of this network are replaced by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a network FWNN modified. In the proposed structure, functions only wavelets are used in the consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of adjustable parameters of the network. To evaluate the performance of network FWNN with this modification, an analysis of network performance is made, verifying advantages, disadvantages and cost effectiveness when compared to other existing FWNN structures in literature. The evaluations are carried out via the identification of two simulated systems traditionally found in the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the network is used to infer values of temperature and humidity inside of a neonatal incubator. The execution of such analyzes is based on various criteria, like: mean squared error, number of training epochs, number of adjustable parameters, the variation of the mean square error, among others. The results found show the generalization ability of the modified structure, despite the simplification performed
Nas ?ltimas d?cadas, as redes neurais t?m se estabelecido como uma das principais ferramentas para a identifica??o de sistemas n?o lineares. Entre os diversos tipos de redes utilizadas em identifica??o, uma que se pode destacar ? a rede neural wavelet (ou Wavelet Neural Network - WNN). Esta rede combina as caracter?sticas de multirresolu??o da teoria wavelet com a capacidade de aprendizado e generaliza??o das redes neurais, podendo fornecer modelos mais exatos do que os obtidos pelas redes tradicionais. Uma evolu??o das redes WNN consiste em combinar a estrutura neuro-fuzzyANFIS (Adaptive Network Based Fuzzy Inference System) com estas redes, gerando-se a estrutura Fuzzy Wavelet Neural Network - FWNN. Essa rede ? muito similar ?s redes ANFIS, com a diferen?a de que os tradicionais polin?mios presentes nos consequentes desta rede s?o substitu?dos por redes WNN. O presente trabalho prop?e uma rede FWNN modificada para a identifica??o de sistemas din?micos n?o lineares. Nessa estrutura, somente fun??es waveletss?o utilizadas nos consequentes. Desta forma, ? poss?vel obter uma simplifica??o da estrutura com rela??o a outras estruturas descritas na literatura, diminuindo o n?mero de par?metros ajust?veis da rede. Para avaliar o desempenho da rede FWNN com essa modifica??o, ? realizada uma an?lise das caracter?sticas da rede, verificando-se as vantagens, desvantagens e o custo-benef?cio quando comparada com outras estruturas FWNNs. As avalia??es s?o realizadas a partir da identifica??o de dois sistemas simulados tradicionalmente encontrados na literatura e um sistema real n?o linear, consistindo de um tanque de multisse??es e n?o linear. Por fim, a rede foi utilizada para inferir valores de temperatura e umidade no interior de uma incubadora neonatal. A execu??o dessa an?lise baseia-se em v?rios crit?rios, tais como: erro m?dio quadr?tico, n?mero de ?pocas de treinamento, n?mero de par?metros ajust?veis, vari?ncia do erro m?dio quadr?tico, entre outros. Os resultados encontrados evidenciam a capacidade de generaliza??o da estrutura modificada, apesar da simplifica??o realizada
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Huang, Ju-Yi, and 黃朱瑜. "RBF Based Neural Fuzzy Network." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/14967502279792003745.

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碩士
國立中興大學
機械工程學系
87
Abstract In this thesis, a new neural fuzzy configuration that combines the RBF neural network structure and fuzzy logic theory is proposed. In this new neural fuzzy structure, the conventional six layers neural fuzzy network is simplified to a four layers neural fuzzy network. For single input problem, this new network structure is a kind of RBF neural network. When a multi-inputs problem is applied, it functions similar a conventional neural fuzzy network. Computer simulation results show that the proposed new neural fuzzy scheme can be successfully applied to the nonlinear function approximation and classification problems. To fulfill the on-line training requirement, an efficient heuristic learning rule is included. Experimental results show that the proposed approach can be successfully applied to the precise regulating and tracking problems of an AC servo motor system. For real industrial application, a systematic approach to achieve global optimal CMP process is carried out. In this new approach, orthogonal array technique in the Taguchi method is adopted for efficient experiment design. The RBFNF neural-fuzzy is then used to model the complex CMP process. Signal to Noise Ratio (S/N) Analysis technique used in the conventional Taguchi method is also implemented to find the local optimal process parameters. Successively, the global optimal parameters are acquired in terms of the trained RBFNF network. In order to increase the CMP throughput, a two-stage optimal strategy is also proposed. Experimental results show that the two-stage strategy can perform better then the original approach even though the process time is reduced by 1/6.
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Lo, Fang-Chun, and 羅方鈞. "Fuzzy Neural Network Based Face Recognition." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/31012850840479560921.

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碩士
國立暨南國際大學
電機工程學系
99
In recent years, image processing and robot vision technology continues to development, and the human face recognition has become an important technology; therefore, this thesis presents a human face recognition system based on fuzzy neural network. The local feature is used as the facial feature parameters.The face recognition method which is presented consists of two steps. First, using the Scale Invariant Feature Transform (SIFT) method to find the features from the pictures. The Scale Invariant Feature Transform (SIFT) method has four parts, Scale-space extrema detection, Keypoint localization, Orientation assignment, Keypoint descriptor. In the second step, we take all features as FNN inputs to make the final decision. In order to training fast that we use the method of binary tree, and build a binary tree. The experiment results display that the method can identification human face efficiently.
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Hsu, Chung-Pin, and 徐崇濱. "Fuzzy Neural Network Based Face Locating." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/14074241759215438268.

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碩士
國立暨南國際大學
電機工程學系
98
This thesis proposes a robust human face locating based on color with fuzzy neural network. First, the color of pictures all transform from RGB color space to HSV color space and only hue and saturation are preferred to the needed color information to eliminate the effect of illumination. Then, get the color information of holes, which are included by segmented skin color, as features for fuzzy neural network. Use those features to classify eyes, mouths and other parts. This kind of way can reduce the region of color that needed to classify for improving the accuracy of recognition. At the same time, rotate the angle of hue belonging to eyes and mouths by 180 degree for getting the average values more accurate. After the eyes and mouths are recognized, pick the most possible relative positions for getting the most accurate human face location. Because this method simply uses the color for judgment, it needs not rely on the integrity of facial shape. Therefore, even if there are other skins connecting with face or something in front of face cause the face shape or facial feature shape to be changed, this method can still be used. As a result, shooting angle need not be completely right in the face and this method does not need continuous images to be discriminated.
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Books on the topic "Neural fuzzy network"

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Campbell, Jonathan George. Fuzzy logic and neural network network techniques in data analysis. [s.l: The Author], 2000.

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Y, Cheung John, ed. Fuzzy engineering expert systems with neural network applications. New York: J. Wiley, 2002.

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Badiru, Adedeji Bodunde. Fuzzy Engineering Expert Systems with Neural Network Applications. New York: John Wiley & Sons, Ltd., 2002.

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Hudgins, Billy E. Implementation of fuzzy inference systems using neural network techniques. Monterey, Calif: Naval Postgraduate School, 1992.

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Neural network and fuzzy logic applications in C/C++. New York: Wiley, 1994.

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Neural network and fuzzy logic applications in C/C++. New York: Wiley, 1994.

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Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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Cios, Krzysztof J. Self-growing neural network architecture using crisp and fuzzy entropy. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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Book chapters on the topic "Neural fuzzy network"

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Rutkowska, Danuta. "Neural Network Architecture of Fuzzy Systems." In Fuzzy Control, 277–86. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_24.

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Abe, Shigeo. "The Hopfield Network." In Neural Networks and Fuzzy Systems, 7–43. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5_2.

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Zhang, Tianyue, Baile Xu, and Furao Shen. "Fuzzy Self-Organizing Incremental Neural Network for Fuzzy Clustering." In Neural Information Processing, 24–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_3.

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Kim, Kwang-Baek, Hae-Ryong Bea, and Chang-Suk Kim. "A Physiological Fuzzy Neural Network." In Lecture Notes in Computer Science, 1182–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539902_149.

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Kim, Kwang Baek, Young-Hoon Joo, and Jae-Hyun Cho. "An Enhanced Fuzzy Neural Network." In Parallel and Distributed Computing: Applications and Technologies, 176–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30501-9_40.

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Iatan, Iuliana F. "A Recurrent Neural Fuzzy Network." In Issues in the Use of Neural Networks in Information Retrieval, 187–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43871-9_8.

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Azzerboni, Bruno, Mario Carpentieri, Maurizio Ipsale, Fabio La Foresta, and Francesco Carlo Morabito. "Intracranial Pressure Signal Processing by Adaptive Fuzzy Network." In Neural Nets, 179–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45216-4_20.

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Aliev, Rafik, Bijan Fazlollahi, Rashad Aliev, and Babek Guirimov. "Fuzzy Time Series Prediction Method Based on Fuzzy Recurrent Neural Network." In Neural Information Processing, 860–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893257_95.

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Khan, Emdad. "NeuFuz: Fuzzy Logic Design Based on Neural Network Learning." In Fuzzy Logik, 3–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79386-8_1.

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Czogała, Ernest, and Jacek Łęski. "Applications of artificial neural network based fuzzy inference system." In Fuzzy and Neuro-Fuzzy Intelligent Systems, 163–80. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1853-6_7.

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Conference papers on the topic "Neural fuzzy network"

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Kumar, Manish, and Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.

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Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.
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El-Shafei, A., T. A. F. Hassan, A. K. Soliman, Y. Zeyada, and N. Rieger. "Neural Network and Fuzzy Logic Diagnostics of 1X Faults in Rotating Machinery." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68885.

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In this paper, the application of Neural Networks and Fuzzy Logic to the diagnosis of Faults in Rotating Machinery is investigated. The Learning-Vector-Quantization (LVQ) Neural Network is applied in series and in parallel to a Fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig [1], and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.
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Hernandez, Gerardo, Erik Zamora, and Humberto Sossa. "Morphological-Linear Neural Network." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491539.

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Jing, Zhongliang, Albert C. J. Luo, and M. Tomizuka. "A Stochastic, Fuzzy, Neural Network for Unknown Dynamic Systems." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0558.

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Abstract A stochastic, fuzzy, neural network (SFNN) to model unknown nonlinear dynamic systems is developed. In this network, a non-singleton fuzzifier with Gaussian membership functions instead of the singleton fuzzifier in the fuzzy logic is introduced. Based on these membership functions, an online supervised parameter learning algorithm of the SFNN is proposed to overcome the local minimum of learning process in current neural networks, and an off-line algorithm for the structure learning of the SFNN is presented to reduce the amount of computation of the SFNN. This new network provides a universal approximator and it is also applicable to stochastic control and decision systems and the identification of chaos in nonlinear systems.
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Li, C. James, Chong-suhk Lee, and Sun’an Wang. "Diagnosis and Diagnostic Rule Extraction Using Fuzzy Neural Network." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/dsc-24506.

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Abstract The goal of this study is to develop a reasoning device and a diagnostic rule extraction methodology based on fuzzy neural network. This paper describes a method to obtain a fuzzy neural network classifier from labeled training data sets and algorithms to extracted linguistic diagnostic rules from such a trained fuzzy neural network. Benchmark comparisons were performed using three data sets from three different fields of applications. The proposed methodology was shown to outperform all the existing methods that were compared.
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Wang, Yifan, Hisao Ishibuchi, Jihua Zhu, Yaxiong Wang, and Tao Dai. "Unsupervised Fuzzy Neural Network for Image Clustering." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494601.

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Iatan, I. F. "A CONCURRENT FUZZY NEURAL NETWORK APPROACH FOR A FUZZY GAUSSIAN NEURAL NETWORK." In 10th World Congress on Computational Mechanics. São Paulo: Editora Edgard Blücher, 2014. http://dx.doi.org/10.5151/meceng-wccm2012-19128.

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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 significance of this study is to examine the challenges of energy efficient algorithms and the network lifetime on WSN so that they can assist several applications. Clustering is one of the hierarchical based routing protocols, which manage the communication between sensor nodes and sink via Cluster Head (CH), CH is responsible to send and receive information from multiple sensor nodes and multiple base stations (BS). There are various algorithms that can efficiently select appropriate CH and localize the membership of cluster with fuzzy logic classification parameters to minimize periodic clustering which consumes more energy and we have applied neural network learning algorithm to learn various patterns based on the fuzzy rules and measured how much energy has saved from random clustering. Finally, we have compared to our Neuro-Fuzzy logic and consequently demonstrated that our Neuro-Fuzzy model outperforms than random model.
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Demby's, Jacket, Yixiang Gao, and G. N. DeSouza. "A Study on Solving the Inverse Kinematics of Serial Robots using Artificial Neural Network and Fuzzy Neural Network." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858872.

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Polap, Dawid. "Automatic fuzzy parameter tuning for neural network models." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882543.

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Reports on the topic "Neural fuzzy network"

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Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada354805.

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Huang, Z., J. Shimeld, and M. Williamson. Application of computer neural network, and fuzzy set logic to petroleum geology, offshore eastern Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/194121.

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Rajagopalan, A., G. Washington, G. Rizzoni, and Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/15006009.

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Maurer, W. J., and F. U. Dowla. Seismic event interpretation using fuzzy logic and neural networks. Office of Scientific and Technical Information (OSTI), January 1994. http://dx.doi.org/10.2172/10139515.

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