Relevant bibliographies by topics / Fuzzy systems; Neural networks

Contents

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

Academic literature on the topic 'Fuzzy systems; Neural networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Fuzzy systems; Neural networks"

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Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (April 1, 1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.
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Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (June 20, 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, adaptive fuzzy systems, fuzzy inference neural networks, etc. Two different defuzzifiers, applied to fuzzy systems, are in focus of the paper. Center-of-sums method is an example of parametric defuzzification. Standard neural networks a defuzzifier presents nonparametric approach to defuzzification. For both cases learning algorithms of neuro-fuzzy systems are proposed. These algorithms take a form of recursions derived based on the momentum back-propagation method. Computer simulation demonstrates a comparison between performance of neuro-fuzzy systems with the parametric and nonparametric defuzzifier. Truck backer-upper control problem has been used to illustrate the systems performance. Conclusions concerning the simulation results are summarized. The paper pertains many references on neuro-fuzzy systems, especially selected publications of Czogala, whom it is dedicated.
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Godjevac, Jelena, and Nigel Steele. "Fuzzy Systems and Neural Networks." Intelligent Automation & Soft Computing 4, no. 1 (January 1998): 27–37. http://dx.doi.org/10.1080/10798587.1998.10750719.

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Kosko, Bart, and John C. Burgess. "Neural Networks and Fuzzy Systems." Journal of the Acoustical Society of America 103, no. 6 (June 1998): 3131. http://dx.doi.org/10.1121/1.423096.

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Zambelli, Stefano. "Neural networks and fuzzy systems." Journal of Economic Dynamics and Control 17, no. 3 (May 1993): 523–29. http://dx.doi.org/10.1016/0165-1889(93)90010-p.

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Dvorak, V. "Neural networks and fuzzy systems." Knowledge-Based Systems 6, no. 3 (September 1993): 179. http://dx.doi.org/10.1016/0950-7051(93)90043-s.

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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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Blake, J. "The implementation of fuzzy systems, neural networks and fuzzy neural networks using FPGAs." Information Sciences 112, no. 1-4 (December 1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.

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NASRABADI, EBRAHIM, and S. MEHDI HASHEMI. "ROBUST FUZZY REGRESSION ANALYSIS USING NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (August 2008): 579–98. http://dx.doi.org/10.1142/s021848850800542x.

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Some neural network related methods have been applied to nonlinear fuzzy regression analysis by several investigators. The performance of these methods will significantly worsen when the outliers exist in the training data set. In this paper, we propose a training algorithm for fuzzy neural networks with general fuzzy number weights, biases, inputs and outputs for computation of nonlinear fuzzy regression models. First, we define a cost function that is based on the concept of possibility of fuzzy equality between the fuzzy output of fuzzy neural network and the corresponding fuzzy target. Next, a training algorithm is derived from the cost function in a similar manner as the back-propagation algorithm. Last, we examine the ability of our approach by computer simulations on numerical examples. Simulation results show that the proposed algorithm is able to reduce the outlier effects.
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OH, SUNG-KWUN, DONG-WON KIM, and WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 03 (June 2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.

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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x) " where A is fuzzy relation in the condition space while P(x) is a polynomial standing in the conclusion part of the rule. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as constant, linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are considered. Each PN of the network realizes a polynomial type of partial description (PD) of the mapping between input and out variables. HFPNN is a flexible neural architecture whose structure is based on the Group Method of Data Handling (GMDH) and developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. The experimental part of the study involves two representative numerical examples such as chaotic time series and Box-Jenkins gas furnace data.
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Dissertations / Theses on the topic "Fuzzy systems; Neural networks"

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Pirovolou, Dimitrios K. "The tracking problem using fuzzy neural networks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14824.

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Frayman, Yakov, and mikewood@deakin edu au. "Fuzzy neural networks for control of dynamic systems." Deakin University. School of Computing and Mathematics, 1999. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051017.145550.

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This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.
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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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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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Morphet, Steven Brian Işık Can. "Modeling neural networks via linguistically interpretable fuzzy inference systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2004. http://wwwlib.umi.com/cr/syr/main.

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Dias, De Macedo Filho Antonio. "Microwave neural networks and fuzzy classifiers for ES systems." Thesis, University College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244066.

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Hsu, Cheng-Yu. "Condition monitoring of fluid power systems using artificial neural networks." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295443.

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Vetcha, Sarat Babu. "Fault diagnosis in pumps by unsupervised neural networks." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300604.

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Ji, Wei. "Artificial neural networks and fuzzy systems in bladder cancer prognosis." Thesis, Coventry University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417616.

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Ismael, Ali. "Neural adaptive control systems /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9901244.

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Books on the topic "Fuzzy systems; Neural networks"

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Abe, Shigeo. Neural Networks and Fuzzy Systems. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5.

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International conference (February 12-14, 1996 Lausanne, Switzerland). Microeletronics for neural networks and fuzzy systems. Los Alamitos, Calif: IEEE, 1996.

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G, Lee C. S., ed. Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Upper Saddle River, NJ: Prentice Hall PTR, 1996.

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Rao, Valluru. C++ neural networks and fuzzy logic. New York: MIS:Press, 1993.

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Rao, Valluru. C++ neural networks and fuzzy logic. 2nd ed. New York: MIS:Press, 1995.

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Abe, Shigeo. Neural Networks and Fuzzy Systems: Theory and Applications. Boston, MA: Springer US, 1997.

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Neural networks and fuzzy systems: Theory and applications. Boston: Kluwer Academic, 1997.

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Shigeo, Abe. Neural networks and fuzzy systems: Theory and applications. Boston, Mass: Kluwer Academic, 1997.

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A course in fuzzy systems and control. Upper Saddle River, N.J: Prentice Hall PTR, 1997.

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

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Book chapters on the topic "Fuzzy systems; Neural networks"

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Czogała, Ernest, and Jacek Łęski. "Artificial neural networks." In Fuzzy and Neuro-Fuzzy Intelligent Systems, 65–92. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1853-6_3.

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Prasad, Nadipuram Ram R. "Neural Networks and Fuzzy Logic." In Fuzzy Systems, 381–401. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5505-6_11.

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Fullér, Robert. "Fuzzy neural networks." In Introduction to Neuro-Fuzzy Systems, 171–254. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_3.

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Singh, Himanshu, and Yunis Ahmad Lone. "Fuzzy Neural Networks." In Deep Neuro-Fuzzy Systems with Python, 199–221. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5361-8_6.

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Abe, Shigeo. "Other Neural Networks." In Neural Networks and Fuzzy Systems, 93–125. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5_4.

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Fullér, Robert. "Artificial neural networks." In Introduction to Neuro-Fuzzy Systems, 133–70. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_2.

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Abe, Shigeo. "Multilayered Networks." In Neural Networks and Fuzzy Systems, 45–91. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5_3.

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Abe, Shigeo. "Composite Systems." In Neural Networks and Fuzzy Systems, 209–24. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5_8.

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Jin, Yaochu. "Artificial Neural Networks." In Advanced Fuzzy Systems Design and Applications, 73–91. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1771-3_3.

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Singh, Himanshu, and Yunis Ahmad Lone. "Artificial Neural Networks." In Deep Neuro-Fuzzy Systems with Python, 157–98. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5361-8_5.

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Conference papers on the topic "Fuzzy systems; Neural networks"

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Ji-Cheng Duan and Fu-Lai Chung. "Cascading fuzzy neural networks." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793206.

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Aouiti, Chaouki, Farah Dridi, and Fakhri Karray. "New Results on Neutral Type Fuzzy Based Cellular Neural Networks." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491607.

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Romaniuk and Hall. "Fuzzy connectionist expert systems." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118532.

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Aversano, Lerina, Mario Luca Bernardi, Marta Cimitile, and Riccardo Pecori. "Fuzzy Neural Networks to Detect Parkinson Disease." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177948.

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Amina, Mahdi, and Vassilis S. Kodogiannis. "Load forecasting using fuzzy wavelet neural networks." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007492.

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Wang, Jing, Chi-Hsu Wang, and C. L. Philip Chen. "Finding the capacity of Fuzzy Neural Networks (FNNs) via its equivalent fully connected neural networks (FFNNs)." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007473.

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Tsimboukakis, Nikos, and George Tambouratzis. "Neural Networks for Author Attribution." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295356.

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Israel, Cruz Vega, Wen Yu, and Juan Jose Cordova. "Multiple fuzzy neural networks modeling with sparse data." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584804.

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Zhao, Wei Bin, Yue Li, and Lin Shang. "Fuzzy Pruning for Compression of Convolutional Neural Networks." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8858894.

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Fierimonte, Roberto, Marco Barbato, Antonello Rosato, and Massimo Panella. "Distributed learning of Random Weights Fuzzy Neural Networks." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737978.

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Reports on the topic "Fuzzy systems; Neural networks"

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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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Hirsch, Morris W., Bill Baird, Walter Freeman, and Bernice Gangale. Dynamical Systems, Neural Networks and Cortical Models ASSERT 93. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada295495.

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Morgan, Nelson, Jerome Feldman, and John Wawrzynek. Accelerator Systems for Neural Networks, Speech, and Related Applications. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada298954.

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Priebe, C. E., and D. J. Marchette. Experience with Neural Networks at Naval Ocean Systems Center. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada198923.

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Zhuang, Y., and J. S. Baras. Identification of Infinite Dimensional Systems via Adaptive Wavelet Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada454923.

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Mu, Hong H., Y. P. Kakad, and B. G. Sherlock. Application of Artificial Neural Networks in the Design of Control Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada384438.

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Talathi, S. S. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1366924.

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Fisher, Andmorgan, Timothy Middleton, Jonathan Cotugno, Elena Sava, Laura Clemente-Harding, Joseph Berger, Allistar Smith, and Teresa Li. Use of convolutional neural networks for semantic image segmentation across different computing systems. Engineer Research and Development Center (U.S.), March 2020. http://dx.doi.org/10.21079/11681/35881.

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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
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Nikiforov, Vladimir. Laser technology and integrated technical systems in devices and instruments for ophthalmology using elements of artificial intelligence associated with artificial neural networks. Intellectual Archive, May 2019. http://dx.doi.org/10.32370/iaj.2123.

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