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Zeitschriftenartikel zum Thema „Neural computer“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 6. Februar 2022

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1

Nakajima, K., Y. Mizugaki, T. Yamashita und Y. Sawada. „Superconducting neural computer“. Applied Superconductivity 1, Nr. 10-12 (Oktober 1993): 1893–905. http://dx.doi.org/10.1016/0964-1807(93)90337-2.

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2

PĂUN, GHEORGHE, MARIO J. PÉREZ-JIMÉNEZ und GRZEGORZ ROZENBERG. „COMPUTING MORPHISMS BY SPIKING NEURAL P SYSTEMS“. International Journal of Foundations of Computer Science 18, Nr. 06 (Dezember 2007): 1371–82. http://dx.doi.org/10.1142/s0129054107005418.

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We continue the study of the spiking neural P systems considered as transducers of binary strings or binary infinite sequences, and we investigate their ability to compute morphisms. The class of computed morphisms is rather restricted: length preserving or erasing, and the so-called 2-block morphisms can be computed; however, non-erasing non-length-preserving morphisms cannot be computed.

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3

Ziegel, Eric R. „Neural Networks in Computer Intelligence“. Technometrics 37, Nr. 4 (November 1995): 470. http://dx.doi.org/10.1080/00401706.1995.10484401.

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4

de Oliveira, P. M. C., Harvey Gould und Jan Tobochnik. „Computer Simulations of Neural Networks“. Computers in Physics 11, Nr. 5 (1997): 443. http://dx.doi.org/10.1063/1.4822587.

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5

Lirov, Yuval. „Computer aided neural network engineering“. Neural Networks 5, Nr. 4 (Juli 1992): 711–19. http://dx.doi.org/10.1016/s0893-6080(05)80047-4.

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6

Akamatsu, Norio, Yoshihiro Nakamura und Tohru Kawabe. „Neural computer using electromagnetic coupling“. Systems and Computers in Japan 23, Nr. 8 (1992): 85–96. http://dx.doi.org/10.1002/scj.4690230809.

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7

Selviah, David R. „Neural computer uses optical fingers“. Physics World 7, Nr. 7 (Juli 1994): 29–30. http://dx.doi.org/10.1088/2058-7058/7/7/29.

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8

Howlett, R. J., und S. D. Walters. „Multi-computer neural network architecture“. Electronics Letters 35, Nr. 16 (1999): 1350. http://dx.doi.org/10.1049/el:19990962.

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9

Schöneburg, E. „Neural networks hunt computer viruses“. Neurocomputing 2, Nr. 5-6 (Juli 1991): 243–48. http://dx.doi.org/10.1016/0925-2312(91)90027-9.

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10

Kulkarni, Arun D. „Computer Vision and Fuzzy-Neural Systems“. Journal of Electronic Imaging 13, Nr. 1 (01.01.2004): 251. http://dx.doi.org/10.1117/1.1640620.

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11

Hopkins, Michael, Garibaldi Pineda-García, Petruţ A. Bogdan und Steve B. Furber. „Spiking neural networks for computer vision“. Interface Focus 8, Nr. 4 (15.06.2018): 20180007. http://dx.doi.org/10.1098/rsfs.2018.0007.

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State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously, often with a non-uniform resolution, and generate neural spike events in response to changes in the scene. The resulting spatio-temporal patterns of events are then processed through networks of spiking neurons. Such event-based processing offers advantages in terms of focusing constrained resources on the most salient features of the perceived scene, and those advantages should also accrue to engineered vision systems based upon similar principles. Event-based vision sensors, and event-based processing exemplified by the SpiNNaker (Spiking Neural Network Architecture) machine, can be used to model the biological vision pathway at various levels of detail. Here we use this approach to explore structural synaptic plasticity as a possible mechanism whereby biological vision systems may learn the statistics of their inputs without supervision, pointing the way to engineered vision systems with similar online learning capabilities.

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12

Tesauro, G. J., J. O. Kephart und G. B. Sorkin. „Neural networks for computer virus recognition“. IEEE Expert 11, Nr. 4 (August 1996): 5–6. http://dx.doi.org/10.1109/64.511768.

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13

Dunnett, David, Anthony Goodbody und Martin Stanisstreet. „Computer modelling of neural tube defects“. Acta Biotheoretica 39, Nr. 1 (März 1991): 63–79. http://dx.doi.org/10.1007/bf00046408.

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14

Guinier, Daniel. „Computer “virus” identification by neural networks“. ACM SIGSAC Review 9, Nr. 4 (September 1991): 49–59. http://dx.doi.org/10.1145/126569.127021.

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15

Ejaz, Farukh, Muhammad Hussain, Hamad Almohamedh, Khalid M. Alhamed, Rana Alabdan und Sultan Almotairi. „Dominating Topological Analysis and Comparison of the Cellular Neural Network“. Mathematical Problems in Engineering 2021 (16.04.2021): 1–9. http://dx.doi.org/10.1155/2021/6613433.

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Graph theory is a discrete branch of mathematics for designing and predicting a network. Some topological invariants are mathematical tools for the analysis of connection properties of a particular network. The Cellular Neural Network (CNN) is a computer paradigm in the field of machine learning and computer science. In this article we have given a close expression to dominating invariants computed by the dominating degree for a cellular neural network. Moreover, we have also presented a 3D comparison between dominating invariants and classical degree-based indices to show that, in some cases, dominating invariants give a better correlation on the cellular neural network as compared to classical indices.

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16

BODDHU, SANJAY K., JOHN C. GALLAGHER und SARANYAN A. VIGRAHAM. „A COMMERCIAL OFF-THE-SHELF IMPLEMENTATION OF AN ANALOG NEURAL COMPUTER“. International Journal on Artificial Intelligence Tools 17, Nr. 02 (April 2008): 241–58. http://dx.doi.org/10.1142/s021821300800387x.

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For most applications, analog electrical circuit implementations of continuous-valued neural networks have been abandoned in favor of digital simulations. This is not surprising, as both precision and accuracy can be more easily ensured in digital computers. Still, because they use far fewer transistors and support components, analog circuits can still be orders of magnitude smaller than their digital simulations. In some application, like micro-robotics and embedded control, one might be willing to tolerate less accuracy and precision for the size and power benefits. One would not under any condition, however, tolerate significant behavioral mismatches between the differential equation and electrical circuit forms of the neural networks in question. In this paper, we will present a design for an analog neural computer that embodies the commonly used continuous time recurrent neural network. We will show that the computer possesses excellent behavioral congruence to the differential equation form even in the presence of significant practical compromises. We will also discuss the implications of this work for both practical Commercial, Off-The-Shelf (COTS) and Application-Specific Integrated Circuit (ASIC) devices.

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17

Cierniak, Robert. „A New Approach to Image Reconstruction from Projections Using a Recurrent Neural Network“. International Journal of Applied Mathematics and Computer Science 18, Nr. 2 (01.06.2008): 147–57. http://dx.doi.org/10.2478/v10006-008-0014-y.

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A New Approach to Image Reconstruction from Projections Using a Recurrent Neural NetworkA new neural network approach to image reconstruction from projections considering the parallel geometry of the scanner is presented. To solve this key problem in computed tomography, a special recurrent neural network is proposed. The reconstruction process is performed during the minimization of the energy function in this network. The performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in the obtained image quality.

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18

Li, Xiao Guang. „Research on the Development and Applications of Artificial Neural Networks“. Applied Mechanics and Materials 556-562 (Mai 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.

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19

Cesar, Roberto Marcondes, und Luciano da Fontoura Costa. „Computer-vision-based extraction of neural dendrograms“. Journal of Neuroscience Methods 93, Nr. 2 (November 1999): 121–31. http://dx.doi.org/10.1016/s0165-0270(99)00120-x.

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20

Halder, S., D. Agorastos, R. Veit, E. M. Hammer, S. Lee, B. Varkuti, M. Bogdan, W. Rosenstiel, N. Birbaumer und A. Kübler. „Neural mechanisms of brain–computer interface control“. NeuroImage 55, Nr. 4 (April 2011): 1779–90. http://dx.doi.org/10.1016/j.neuroimage.2011.01.021.

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21

Igor, Halenar, Juhasova Bohuslava, Juhas Martin und Nesticky Martin. „Application of Neural Networks in Computer Security“. Procedia Engineering 69 (2014): 1209–15. http://dx.doi.org/10.1016/j.proeng.2014.03.111.

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22

Cottrell, G. W. „COMPUTER SCIENCE: New Life for Neural Networks“. Science 313, Nr. 5786 (28.07.2006): 454–55. http://dx.doi.org/10.1126/science.1129813.

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23

RICHARDSON, C. J., und D. J. BARLOW. „Neural Network Computer Simulation of Medical Aerosols“. Journal of Pharmacy and Pharmacology 48, Nr. 6 (Juni 1996): 581–91. http://dx.doi.org/10.1111/j.2042-7158.1996.tb05978.x.

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24

Ringwood, J. V., und G. Galvin. „Computer-aided learning in artificial neural networks“. IEEE Transactions on Education 45, Nr. 4 (November 2002): 380–87. http://dx.doi.org/10.1109/te.2002.804401.

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25

Cox, David Daniel, und Thomas Dean. „Neural Networks and Neuroscience-Inspired Computer Vision“. Current Biology 24, Nr. 18 (September 2014): R921—R929. http://dx.doi.org/10.1016/j.cub.2014.08.026.

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26

Yokoi, Hirokazu. „A fundamental element for neural computer - Folthret“. Neurocomputing 6, Nr. 4 (August 1994): 473–86. http://dx.doi.org/10.1016/0925-2312(94)90023-x.

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27

Just, D., und D. T. Ling. „Neural networks for binarizing computer-generated holograms“. Optics Communications 81, Nr. 1-2 (Februar 1991): 1–5. http://dx.doi.org/10.1016/0030-4018(91)90282-i.

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28

Yashchenko, V. A. „Multidimensional neural growing networks and computer intelligence“. Cybernetics and Systems Analysis 30, Nr. 4 (Juli 1994): 505–17. http://dx.doi.org/10.1007/bf02366560.

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29

Loo, Chu Kiong, Mitja Peruš und Horst Bischof. „Associative Memory Based Image and Object Recognition by Quantum Holography“. Open Systems & Information Dynamics 11, Nr. 03 (September 2004): 277–89. http://dx.doi.org/10.1023/b:opsy.0000047571.17774.8d.

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A quantum associative memory, much more natural than those of “quantum computers”, is presented. Neural-net-like processing with real-valued variables is transformed into processing with quantum waves. Successful computer simulations of image storage and retrieval are reported. Our Hopfield-like algorithm allows quantum implementation with holographic procedure using present-day quantum-optics techniques. This brings many advantages over classical Hopfield neural nets and quantum computers with logic gates.

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30

Liu, Jing, Yu Chi Zhao, Xiao Hua Shi und Su Juan Liu. „The Application of Neural Network in Computer Control“. Applied Mechanics and Materials 380-384 (August 2013): 421–24. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.421.

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In recent years, it is a very active direction of research to use neural network to control computer. Neural network is a burgeoning crossing subject, and the way it processes information is different from the past symbolic logic system, which has some unique properties: such as the distributed storage and parallel processing of information, the unity of the information storage and information processing, and have the ability of self-organizing and self-learning. And it has been applied widespread in pattern recognition, signal processing, knowledge process, expert system, optimization, intelligent control and so on. Using neural network can deal with some problems such as complicated environment information, fuzzy background knowledge and undefined inference rules, and it allows samples to have relatively large defects and distortion, so it is a very good choice to adopt the recognizing method of neural network. This thesis discusses the application of neural network in computer control.

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31

Cornu, Thierry, Paolo Ienne, Dagmar Niebur, Patrick Thiran und Marc A. Viredaz. „Design, Implementation, and Test of a Multi-Model Systolic Neural-Network Accelerator“. Scientific Programming 5, Nr. 1 (1996): 47–61. http://dx.doi.org/10.1155/1996/189626.

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A multi-model neural-network computer has been designed and built. A compute-intensive application in the field of power-system monitoring, using the Kohonen neural network, has then been ported onto this machine. After a short description of the system, this article focuses on the programming paradigm adopted. The performance of the machine is also evaluated and discussed.

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32

Chen, Yuan, und Guo Biao Ren. „Computer Network Fault Detection Based on Neural Network“. Advanced Materials Research 889-890 (Februar 2014): 1279–83. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1279.

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This document explains that the neural network has good nonlinear mapping and adaptive capacity. It is becoming more and more widely applied in computer network fault detection. The article take BP neural network as an example to illustrate how to detect the computer network faults.

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33

Wang, Xun, und Jie Rong. „The Computer Network Optimization Model Based on Neural Network Algorithm Research“. Advanced Materials Research 798-799 (September 2013): 545–48. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.545.

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The speed of development of the computer network is an urgent need to comprehensively improve and optimize the overall performance of the network. Neural network algorithm has a massively parallel processing and distributed information storage, Hopfield neural network showed a unique advantage in the associative memory and optimization based on the neural network algorithm for computer network optimization model of Hopfield neural network theory and reality computer network, modern optimization methods, it is combined.

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34

CAVALIERI, S., A. DI STEFANO und O. MIRABELLA. „NEURAL STRATEGIES TO HANDLE ROUTING IN COMPUTER NETWORKS“. International Journal of Neural Systems 04, Nr. 03 (September 1993): 269–89. http://dx.doi.org/10.1142/s0129065793000225.

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In this paper, the authors adopt a neural approach to deal with the problem of routing in a packet switching network. The aim is to define a routing strategy which will combine the advantages of both the centralized and the distributed approaches. The neural approach presented is based on the idea of inserting a neural network (N/N) into each node in the computer network which will be responsible for computing the route between its node and the immediately adjacent one. Two distributed routing solutions are presented in the paper based on an optimizing network and a mapping network. The routing obtainable and the implementation resources needed for the two approaches are evaluated. Finally, the performance offered by the neural strategies proposed is compared with that offered by classical distributed and centralized routing solutions. As a parameter of merit, the effect of overloading caused by the additional traffic present in each solution is used.

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35

Udod, Oleksandr, und Hanna Voronina. „DENTAL CARIES PROGNOSIS BY NEURAL NETWORK COMPUTER TECHNOLOGIES“. EUREKA: Health Sciences 6 (30.11.2019): 15–21. http://dx.doi.org/10.21303/2504-5679.2019.001070.

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Computer technologies are widely implemented in clinical dental practice. The use of computer neural network programs in predicting dental caries as the most common dental disease is quite relevant. The aim – to study the effectiveness of using the “CariesPro” computer program developed using neural network technologies in the individual prediction of dental caries in persons of all ages. Materials and methods. We examined 73 persons aged 6–7, 12–15 and 35–44 years, in which the intensity of dental caries was determined taking into account the number of cavities, the hygiene condition of the oral cavity, the structural and functional acid resistance of the enamel of the teeth according to the enamel resistance test and its functional component. The data were added to a neural based computer software program “CariesPro” designed to predict dental caries. After 1 year, a second examination was performed and the dental caries obtained were compared with the individually predicted computer program. Results. The highest intensity of dental caries was found in persons aged 35–44 – 6.69±0.38, in children 6–7 and 12–15 years it was 3.85±0.27 and 2.15±0.24, respectively (p <0.05). After 1 year, the corresponding intensity indices for persons of these age categories were 8.92±0.52; 6.27±0.35 and 4.23±0.2. The growth rates of caries intensity were, respectively, 2.23±0.25; 2.42±0.15 and 2.09±0.15. After comparing the re-survey data with the computer-programmed estimate, the probable number of carious cavities was found to be 61 true and 12 false predictions from the entire sample, the prediction accuracy of the constructed and trained neural network was 83.56 %. Conclusion. The “CariesPro” computer program, developed using neural network technologies, allows to predict the number of carious lesions in a year with a probability of 83.56 %.

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36

Liu, Jia-Bao, Jing Zhao, Shaohui Wang, M. Javaid und Jinde Cao. „On the Topological Properties of the Certain Neural Networks“. Journal of Artificial Intelligence and Soft Computing Research 8, Nr. 4 (01.10.2018): 257–68. http://dx.doi.org/10.1515/jaiscr-2018-0016.

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Abstract A topological index is a numeric quantity associated with a network or a graph that characterizes its whole structural properties. In [Javaid and Cao, Neural Computing and Applications, DOI 10.1007/s00521-017-2972-1], the various degree-based topological indices for the probabilistic neural networks are studied. We extend this study by considering the calculations of the other topological indices, and derive the analytical closed formulas for these new topological indices of the probabilistic neural network. Moreover, a comparative study using computer-based graphs has been carried out first time to clarify the nature of the computed topological descriptors for the probabilistic neural networks. Our results extend some known conclusions.

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37

Ravikumar, D., V. Devi und Arun Raaza. „Development of Brain Computer Interface, using Neural Network“. Research Journal of Pharmacy and Technology 11, Nr. 10 (2018): 4397. http://dx.doi.org/10.5958/0974-360x.2018.00804.1.

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38

Shihab, Khalil. „A Backpropagation Neural Network for Computer Network Security“. Journal of Computer Science 2, Nr. 9 (01.09.2006): 710–15. http://dx.doi.org/10.3844/jcssp.2006.710.715.

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39

Grossberg, Stephen, und Ennio Mingolla. „Computer simulation of neural networks for perceptual psychology“. Behavior Research Methods, Instruments, & Computers 18, Nr. 6 (November 1986): 601–7. http://dx.doi.org/10.3758/bf03201435.

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40

Abramov, Nikolai, und Vitaly Fralenko. „Neural network data protection system for computer systems“. Program Systems: Theory and Applications 8, Nr. 4 (2017): 197–207. http://dx.doi.org/10.25209/2079-3316-2017-8-1-197-207.

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41

Abramov, Nikolai, und Vitaly Fralenko. „Neural network data protection system for computer systems“. Program Systems: Theory and Applications 8, Nr. 4 (2017): 197–207. http://dx.doi.org/10.25209/2079-3316-2017-8-4-197-207.

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42

Schneider, Gisbert, und Paul Wrede. „Artificial neural networks for computer-based molecular design“. Progress in Biophysics and Molecular Biology 70, Nr. 3 (November 1998): 175–222. http://dx.doi.org/10.1016/s0079-6107(98)00026-1.

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43

Pierre, S., H. Said und W. G. Probst. „Routing in computer networks using artificial neural networks“. Artificial Intelligence in Engineering 14, Nr. 4 (Oktober 2000): 295–305. http://dx.doi.org/10.1016/s0954-1810(00)00014-5.

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44

Antón-Rodríguez, M., D. González-Ortega, F. J. Díaz-Pernas, M. Martínez-Zarzuela, I. de la Torre-Díez, D. Boto-Giralda und J. F. Díez-Higuera. „Bio-inspired computer vision based on neural networks“. Pattern Recognition and Image Analysis 21, Nr. 2 (Juni 2011): 108–12. http://dx.doi.org/10.1134/s1054661811020064.

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45

Turega, M. „A Computer Architecture to Support Neural Net Simulation“. Computer Journal 35, Nr. 4 (01.08.1992): 353–60. http://dx.doi.org/10.1093/comjnl/35.4.353.

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46

Seiffert, Udo. „Artificial neural networks on massively parallel computer hardware“. Neurocomputing 57 (März 2004): 135–50. http://dx.doi.org/10.1016/j.neucom.2004.01.011.

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47

Bishop, J. M., M. J. Bushnell und S. Westland. „Application of neural networks to computer recipe prediction“. Color Research & Application 16, Nr. 1 (Februar 1991): 3–9. http://dx.doi.org/10.1002/col.5080160104.

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48

Wang, Xiaopeng, Lei Gu und Zhongyi Wang. „Computer Medical Image Segmentation Based on Neural Network“. IEEE Access 8 (2020): 158778–86. http://dx.doi.org/10.1109/access.2020.3015541.

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49

Mango, Laurie J. „Computer-assisted cervical cancer screening using neural networks“. Cancer Letters 77, Nr. 2-3 (15.03.1994): 155–62. http://dx.doi.org/10.1016/0304-3835(94)90098-1.

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50

Qian, Wang. „Computer Network Fault Diagnosis Based On Neural Network“. International Journal of Future Generation Communication and Networking 8, Nr. 5 (30.10.2015): 39–50. http://dx.doi.org/10.14257/ijfgcn.2015.8.5.04.

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