Auswahl der wissenschaftlichen Literatur zum Thema „Neural networks (Computer science)“
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Zeitschriftenartikel zum Thema "Neural networks (Computer science)":
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.
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.
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.
Turega, M. A. „Neural Networks“. Computer Journal 35, Nr. 3 (01.06.1992): 290. http://dx.doi.org/10.1093/comjnl/35.3.290.
Widrow, Bernard, David E. Rumelhart und Michael A. Lehr. „Neural networks“. Communications of the ACM 37, Nr. 3 (März 1994): 93–105. http://dx.doi.org/10.1145/175247.175257.
Cavallaro, Lucia, Ovidiu Bagdasar, Pasquale De Meo, Giacomo Fiumara und Antonio Liotta. „Artificial neural networks training acceleration through network science strategies“. Soft Computing 24, Nr. 23 (09.09.2020): 17787–95. http://dx.doi.org/10.1007/s00500-020-05302-y.
Kumar, G. Prem, und P. Venkataram. „Network restoration using recurrent neural networks“. International Journal of Network Management 8, Nr. 5 (September 1998): 264–73. http://dx.doi.org/10.1002/(sici)1099-1190(199809/10)8:5<264::aid-nem298>3.0.co;2-o.
Yen, Gary G., und Haiming Lu. „Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design“. International Journal of Computational Intelligence and Applications 03, Nr. 03 (September 2003): 213–32. http://dx.doi.org/10.1142/s1469026803000975.
SIEGELMANN, HAVA T. „ON NIL: THE SOFTWARE CONSTRUCTOR OF NEURAL NETWORKS“. Parallel Processing Letters 06, Nr. 04 (Dezember 1996): 575–82. http://dx.doi.org/10.1142/s0129626496000510.
Cerf, Vinton G. „On neural networks“. Communications of the ACM 61, Nr. 7 (25.06.2018): 7. http://dx.doi.org/10.1145/3224195.
Dissertationen zum Thema "Neural networks (Computer science)":
Landassuri, Moreno Victor Manuel. „Evolution of modular neural networks“. Thesis, University of Birmingham, 2012. http://etheses.bham.ac.uk//id/eprint/3243/.
Sloan, Cooper Stokes. „Neural bus networks“. Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119711.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 65-68).
Bus schedules are unreliable, leaving passengers waiting and increasing commute times. This problem can be solved by modeling the traffic network, and delivering predicted arrival times to passengers. Research attempts to model traffic networks use historical, statistical and learning based models, with learning based models achieving the best results. This research compares several neural network architectures trained on historical data from Boston buses. Three models are trained: multilayer perceptron, convolutional neural network and recurrent neural network. Recurrent neural networks show the best performance when compared to feed forward models. This indicates that neural time series models are effective at modeling bus networks. The large amount of data available for training bus network models and the effectiveness of large neural networks at modeling this data show that great progress can be made in improving commutes for passengers.
by Cooper Stokes Sloan.
M. Eng.
Khan, Altaf Hamid. „Feedforward neural networks with constrained weights“. Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/4332/.
Zaghloul, Waleed A. Lee Sang M. „Text mining using neural networks“. Lincoln, Neb. : University of Nebraska-Lincoln, 2005. http://0-www.unl.edu.library.unl.edu/libr/Dissertations/2005/Zaghloul.pdf.
Title from title screen (sites viewed on Oct. 18, 2005). PDF text: 100 p. : col. ill. Includes bibliographical references (p. 95-100 of dissertation).
Hadjifaradji, Saeed. „Learning algorithms for restricted neural networks“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0016/NQ48102.pdf.
Cheung, Ka Kit. „Neural networks for optimization“. HKBU Institutional Repository, 2001. http://repository.hkbu.edu.hk/etd_ra/291.
Ahamed, Woakil Uddin. „Quantum recurrent neural networks for filtering“. Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2411.
Williams, Bryn V. „Evolutionary neural networks : models and applications“. Thesis, Aston University, 1995. http://publications.aston.ac.uk/10635/.
De, Jongh Albert. „Neural network ensembles“. Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50035.
ENGLISH ABSTRACT: It is possible to improve on the accuracy of a single neural network by using an ensemble of diverse and accurate networks. This thesis explores diversity in ensembles and looks at the underlying theory and mechanisms employed to generate and combine ensemble members. Bagging and boosting are studied in detail and I explain their success in terms of well-known theoretical instruments. An empirical evaluation of their performance is conducted and I compare them to a single classifier and to each other in terms of accuracy and diversity.
AFRIKAANSE OPSOMMING: Dit is moontlik om op die akkuraatheid van 'n enkele neurale netwerk te verbeter deur 'n ensemble van diverse en akkurate netwerke te gebruik. Hierdie tesis ondersoek diversiteit in ensembles, asook die meganismes waardeur lede van 'n ensemble geskep en gekombineer kan word. Die algoritmes "bagging" en "boosting" word in diepte bestudeer en hulle sukses word aan die hand van bekende teoretiese instrumente verduidelik. Die prestasie van hierdie twee algoritmes word eksperimenteel gemeet en hulle akkuraatheid en diversiteit word met 'n enkele netwerk vergelyk.
Nareshkumar, Nithyalakshmi. „Simulataneous versus Successive Learning in Neural Networks“. Miami University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=miami1134068959.
Bücher zum Thema "Neural networks (Computer science)":
Picton, Philip. Neural networks. New York: Palgrave, 2000.
Abdi, Hervé. Neural networks. Thousand Oaks, Calif: Sage Publications, 1999.
1931-, Taylor John, und UNICOM Seminars, Hrsg. Neural networks. Henley-on-Thames: A. Waller, 1995.
Bischof, Horst. Pyramidal neural networks. Mahwah, NJ: Lawrence Erlbaum Associates, 1995.
Skapura, David M. Building neural networks. New York, N.Y: ACM Press, 1996.
1948-, Vandewalle J., und Roska T, Hrsg. Cellular neural networks. Chichester [England]: Wiley, 1993.
Kwon, Seoyun J. Artificial neural networks. Hauppauge, N.Y: Nova Science Publishers, 2010.
Maass, Wolfgang, 1949 Aug. 21- und Bishop Christopher M, Hrsg. Pulsed neural networks. Cambridge, Mass: MIT Press, 1999.
Hoffmann, Norbert. Simulating neural networks. Wiesbaden: Vieweg, 1994.
Caudill, Maureen. Understanding neural networks: Computer explorations. Cambridge, Mass: MIT Press, 1993.
Buchteile zum Thema "Neural networks (Computer science)":
ElAarag, Hala. „Neural Networks“. In SpringerBriefs in Computer Science, 11–16. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4893-7_3.
Siegelmann, Hava T. „Recurrent neural networks“. In Computer Science Today, 29–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0015235.
Ertel, Wolfgang. „Neural Networks“. In Undergraduate Topics in Computer Science, 221–56. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-299-5_9.
Ertel, Wolfgang. „Neural Networks“. In Undergraduate Topics in Computer Science, 245–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58487-4_9.
Feldman, Jerome A. „Neural Networks and Computer Science“. In Opportunities and Constraints of Parallel Computing, 37–38. New York, NY: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-9668-0_10.
Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz Mostaghim und Matthias Steinbrecher. „General Neural Networks“. In Texts in Computer Science, 37–46. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7296-3_4.
Kruse, Rudolf, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher und Pascal Held. „General Neural Networks“. In Texts in Computer Science, 37–46. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5013-8_4.
Kruse, Rudolf, Sanaz Mostaghim, Christian Borgelt, Christian Braune und Matthias Steinbrecher. „General Neural Networks“. In Texts in Computer Science, 39–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-42227-1_4.
Betti, Alessandro, Marco Gori und Stefano Melacci. „Foveated Neural Networks“. In SpringerBriefs in Computer Science, 63–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90987-1_4.
Betti, Alessandro, Marco Gori und Stefano Melacci. „Foveated Neural Networks“. In SpringerBriefs in Computer Science, 63–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90987-1_4.
Konferenzberichte zum Thema "Neural networks (Computer science)":
Doncow, Sergey, Leonid Orbachevskyi, Valentin Birukow und Nina V. Stepanova. „Artificial Kohonen's neural networks for computer capillarometry“. In Optical Information Science and Technology, herausgegeben von Andrei L. Mikaelian. SPIE, 1998. http://dx.doi.org/10.1117/12.304962.
Shastri, Bhavin J., Volker Sorger und Nir Rotenberg. „In situ Training of Silicon Photonic Neural Networks: from Classical to Quantum“. In CLEO: Science and Innovations. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_si.2023.sm4j.1.
Nowak, Jakub, Marcin Korytkowski und Rafał Scherer. „Classification of Computer Network Users with Convolutional Neural Networks“. In 2018 Federated Conference on Computer Science and Information Systems. IEEE, 2018. http://dx.doi.org/10.15439/2018f321.
Dias, L. P., J. J. F. Cerqueira, K. D. R. Assis und R. C. Almeida. „Using artificial neural network in intrusion detection systems to computer networks“. In 2017 9th Computer Science and Electronic Engineering (CEEC). IEEE, 2017. http://dx.doi.org/10.1109/ceec.2017.8101615.
Araújo, Georger, und Célia Ralha. „Computer Forensic Document Clustering with ART1 Neural Networks“. In The Sixth International Conference on Forensic Computer Science. ABEAT, 2011. http://dx.doi.org/10.5769/c2011011.
Wang, Huiran, und Ruifang Ma. „Optimization of Neural Networks for Network Intrusion Detection“. In 2009 First International Workshop on Education Technology and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.102.
Sakas, D. P., D. S. Vlachos, T. E. Simos, Theodore E. Simos und George Psihoyios. „Fuzzy Neural Networks for Decision Support in Negotiation“. In INTERNATIONAL ELECTRONIC CONFERENCE ON COMPUTER SCIENCE. AIP, 2008. http://dx.doi.org/10.1063/1.3037115.
„Speech Emotion Recognition using Convolutional Neural Networks and Recurrent Neural Networks with Attention Model“. In 2019 the 9th International Workshop on Computer Science and Engineering. WCSE, 2019. http://dx.doi.org/10.18178/wcse.2019.06.044.
Mendez, Arturo J., Emilio G. Rosello, Maria J. Lado, Jacinto G. Dacosta, David M. Torres und Manuel P. Cota. „IMO.Net Artificial Neural Networks: an object-oriented reusable software component library to integrate Matlab Neural Networks functionality“. In Proceedings. 7th Mexican International Conference on Computer Science. IEEE, 2006. http://dx.doi.org/10.1109/enc.2006.18.
Miao, Lihua. „Chaotifiation of Cardiac Dynamics Based on Fuzzy BP Neural Networks“. In Computer Science and Technology 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.81.17.
Berichte der Organisationen zum Thema "Neural networks (Computer science)":
Markova, Oksana, Serhiy Semerikov und Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, Mai 2018. http://dx.doi.org/10.31812/0564/2250.
Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev und Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], Juni 2019. http://dx.doi.org/10.31812/123456789/3178.
Grossberg, Stephen. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics. Fort Belvoir, VA: Defense Technical Information Center, Oktober 1987. http://dx.doi.org/10.21236/ada189981.
Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo und Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.
Farhi, Edward, und Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, Dezember 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.
Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 1996. http://dx.doi.org/10.55274/r0010423.
Modlo, Yevhenii O., Serhiy O. Semerikov, Ruslan P. Shajda, Stanislav T. Tolmachev und Oksana M. Markova. Methods of using mobile Internet devices in the formation of the general professional component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], Juli 2020. http://dx.doi.org/10.31812/123456789/3878.
SAINI, RAVINDER, AbdulKhaliq Alshadid und Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, Dezember 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.
Johansen, Richard, Alan Katzenmeyer, Kaytee Pokrzywinski und Molly Reif. A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs. Engineer Research and Development Center (U.S.), Juli 2023. http://dx.doi.org/10.21079/11681/47261.
Seginer, Ido, James Jones, Per-Olof Gutman und Eduardo Vallejos. Optimal Environmental Control for Indeterminate Greenhouse Crops. United States Department of Agriculture, August 1997. http://dx.doi.org/10.32747/1997.7613034.bard.