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Статті в журналах з теми "Neural Networks method"
Klyuchko, O. M. "APPLICATION OF ARTIFICIAL NEURAL NETWORKS METHOD IN BIOTECHNOLOGY." Biotechnologia Acta 10, no. 4 (August 2017): 5–13. http://dx.doi.org/10.15407/biotech10.04.005.
Повний текст джерелаLi, Keping, Shuang Gu, and Dongyang Yan. "A Link Prediction Method Based on Neural Networks." Applied Sciences 11, no. 11 (June 3, 2021): 5186. http://dx.doi.org/10.3390/app11115186.
Повний текст джерелаGolubinskiy, Andrey, and Andrey Tolstykh. "Hybrid method of conventional neural network training." Informatics and Automation 20, no. 2 (March 30, 2021): 463–90. http://dx.doi.org/10.15622/ia.2021.20.2.8.
Повний текст джерелаJORGENSEN, THOMAS D., BARRY P. HAYNES, and CHARLOTTE C. F. NORLUND. "PRUNING ARTIFICIAL NEURAL NETWORKS USING NEURAL COMPLEXITY MEASURES." International Journal of Neural Systems 18, no. 05 (October 2008): 389–403. http://dx.doi.org/10.1142/s012906570800166x.
Повний текст джерелаPeng, Yun, and Zonglin Zhou. "A neural network learning method for belief networks." International Journal of Intelligent Systems 11, no. 11 (December 7, 1998): 893–915. http://dx.doi.org/10.1002/(sici)1098-111x(199611)11:11<893::aid-int3>3.0.co;2-u.
Повний текст джерелаNeruda, M., and R. Neruda. "To contemplate quantitative and qualitative water features by neural networks method." Plant, Soil and Environment 48, No. 7 (December 21, 2011): 322–26. http://dx.doi.org/10.17221/4375-pse.
Повний текст джерелаFeng, Yifan, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. "Hypergraph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3558–65. http://dx.doi.org/10.1609/aaai.v33i01.33013558.
Повний текст джерелаFan, Yuanliang, Han Wu, Weiming Chen, Zeyu Jiang, Xinghua Huang, and Si-Zhe Chen. "A Data Augmentation Method to Optimize Neural Networks for Predicting SOH of Lithium Batteries." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012034. http://dx.doi.org/10.1088/1742-6596/2203/1/012034.
Повний текст джерелаShi, Lin, and Lei Zheng. "An IGWOCNN Deep Method for Medical Education Quality Estimating." Mathematical Problems in Engineering 2022 (August 9, 2022): 1–5. http://dx.doi.org/10.1155/2022/9037726.
Повний текст джерелаYang, Yunfeng, and Fengxian Tang. "Network Intrusion Detection Based on Stochastic Neural Networks Method." International Journal of Security and Its Applications 10, no. 8 (August 31, 2016): 435–46. http://dx.doi.org/10.14257/ijsia.2016.10.8.38.
Повний текст джерелаДисертації з теми "Neural Networks method"
Dunn, Nathan A. "A Novel Neural Network Analysis Method Applied to Biological Neural Networks." Thesis, view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1251892251&sid=2&Fmt=2&clientId=11238&RQT=309&VName=PQD.
Повний текст джерелаTypescript. Includes vita and abstract. Includes bibliographical references (leaves 122- 131). Also available for download via the World Wide Web; free to University of Oregon users.
Chen, Youping. "Neural network approximation for linear fitting method." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1172243968.
Повний текст джерелаCUNHA, JOAO MARCO BRAGA DA. "ESTIMATING ARTIFICIAL NEURAL NETWORKS WITH GENERALIZED METHOD OF MOMENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26922@1.
Повний текст джерелаCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
As Redes Neurais Artificiais (RNAs) começaram a ser desenvolvidas nos anos 1940. Porém, foi a partir dos anos 1980, com a popularização e o aumento de capacidade dos computadores, que as RNAs passaram a ter grande relevância. Também nos anos 1980, houve dois outros acontecimentos acadêmicos relacionados ao presente trabalho: (i) um grande crescimento do interesse de econometristas por modelos não lineares, que culminou nas abordagens econométricas para RNAs, no final desta década; e (ii) a introdução do Método Generalizado dos Momentos (MGM) para estimação de parâmetros, em 1982. Nas abordagens econométricas de RNAs, sempre predominou a estimação por Quasi Máxima Verossimilhança (QMV). Apesar de possuir boas propriedades assintóticas, a QMV é muito suscetível a um problema nas estimações em amostra finita, conhecido como sobreajuste. O presente trabalho estende o estado da arte em abordagens econométricas de RNAs, apresentando uma proposta alternativa à estimação por QMV que preserva as suas boas propriedades assintóticas e é menos suscetível ao sobreajuste. A proposta utiliza a estimação pelo MGM. Como subproduto, a estimação pelo MGM possibilita a utilização do chamado Teste J para verifificar a existência de não linearidade negligenciada. Os estudos de Monte Carlo realizados indicaram que as estimações pelo MGM são mais precisas que as geradas pela QMV em situações com alto ruído, especialmente em pequenas amostras. Este resultado é compatível com a hipótese de que o MGM é menos suscetível ao sobreajuste. Experimentos de previsão de taxas de câmbio reforçaram estes resultados. Um segundo estudo de Monte Carlo apontou boas propriedades em amostra finita para o Teste J aplicado à não linearidade negligenciada, comparado a um teste de referência amplamente conhecido e utilizado. No geral, os resultados apontaram que a estimação pelo MGM é uma alternativa recomendável, em especial no caso de dados com alto nível de ruído.
Artificial Neural Networks (ANN) started being developed in the decade of 1940. However, it was during the 1980 s that the ANNs became relevant, pushed by the popularization and increasing power of computers. Also in the 1980 s, there were two other two other academic events closely related to the present work: (i) a large increase of interest in nonlinear models from econometricians, culminating in the econometric approaches for ANN by the end of that decade; and (ii) the introduction of the Generalized Method of Moments (GMM) for parameter estimation in 1982. In econometric approaches for ANNs, the estimation by Quasi Maximum Likelihood (QML) always prevailed. Despite its good asymptotic properties, QML is very prone to an issue in finite sample estimations, known as overfiting. This thesis expands the state of the art in econometric approaches for ANNs by presenting an alternative to QML estimation that keeps its good asymptotic properties and has reduced leaning to overfiting. The presented approach relies on GMM estimation. As a byproduct, GMM estimation allows the use of the so-called J Test to verify the existence of neglected nonlinearity. The performed Monte Carlo studies indicate that the estimates from GMM are more accurate than those generated by QML in situations with high noise, especially in small samples. This result supports the hypothesis that GMM is susceptible to overfiting. Exchange rate forecasting experiments reinforced these findings. A second Monte Carlo study revealed satisfactory finite sample properties of the J Test applied to the neglected nonlinearity, compared with a reference test widely known and used. Overall, the results indicated that the estimation by GMM is a better alternative, especially for data with high noise level.
Bishop, Russell C. "A Method for Generating Robot Control Systems." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1222394834.
Повний текст джерелаKAIMAL, VINOD GOPALKRISHNA. "A NEURAL METHOD OF COMPUTING OPTICAL FLOW BASED ON GEOMETRIC CONSTRAINTS." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1037632137.
Повний текст джерелаSung, Woong Je. "A neural network construction method for surrogate modeling of physics-based analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43721.
Повний текст джерелаChavali, Krishna Kumar. "Integration of statistical and neural network method for data analysis." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4749.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains viii, 68 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 50-51).
Radhakrishnan, Kapilan. "A non-intrusive method to evaluate perceived voice quality of VoIP networks using random neural networks." Thesis, Glasgow Caledonian University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547414.
Повний текст джерелаMohamed, Ibrahim. "A method for the analysis of the MDTF data using neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0032/MQ62402.pdf.
Повний текст джерелаRowlands, H. "Optimum design using the Taguchi method with neural networks and genetic algorithms." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241701.
Повний текст джерелаКниги з теми "Neural Networks method"
Zhang, Yunong. Zhang neural networks and neural-dynamic method. Hauppauge, N.Y: Nova Science Publishers, 2009.
Знайти повний текст джерелаHarrison, R. F. A general method for the discovery and use of rules induced by feedforward artificial neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.
Знайти повний текст джерелаAmezcua, Jonathan, Patricia Melin, and Oscar Castillo. New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73773-7.
Повний текст джерелаCronley, Thomas J. The use of neural networks as a method of correlating thermal fluid data to provide useful information on thermal systems. Monterey, Calif: Naval Postgraduate School, 2000.
Знайти повний текст джерелаNeural networks and simulation methods. New York: M. Dekker, 1994.
Знайти повний текст джерелаSuzuki, T. Edge detection methods using neural networks. Manchester: UMIST, 1996.
Знайти повний текст джерелаKumar, Bose Deb, ed. Neural networks: Deterministic methods of analysis. London: International Thomson Computer Press, 1996.
Знайти повний текст джерелаShepherd, Adrian J. Second-Order Methods for Neural Networks. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0953-2.
Повний текст джерелаPabisek, Ewa. Systemy hybrydowe intergruja̜ce MES i SSN w analizie wybranych problemów mechaniki konstrukcji i materiałów. Kraków: Wydawn. Politechniki Krakowskiej, 2008.
Знайти повний текст джерелаPabisek, Ewa. Systemy hybrydowe intergruja̜ce MES i SSN w analizie wybranych problemów mechaniki konstrukcji i materiałów. Kraków: Wydawn. Politechniki Krakowskiej, 2008.
Знайти повний текст джерелаЧастини книг з теми "Neural Networks method"
Annema, Anne-Johan. "The Vector Decomposition Method." In Feed-Forward Neural Networks, 27–37. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2337-6_2.
Повний текст джерелаda Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, and Silas Franco dos Reis Alves. "Method for Classifying Tomatoes Using Computer Vision and MLP Networks." In Artificial Neural Networks, 253–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43162-8_18.
Повний текст джерелаMagoulas, G. D., M. N. Vrahatis, T. N. Grapsa, and G. S. Androulakis. "A Training Method for Discrete Multilayer Neural Networks." In Mathematics of Neural Networks, 250–54. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6099-9_42.
Повний текст джерелаWei, Hui, Bo Lang, and Qing-song Zuo. "A Scale-Changeable Image Analysis Method." In Engineering Applications of Neural Networks, 63–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23957-1_7.
Повний текст джерелаBader, Sebastian, and Steffen Hölldobler. "The Core Method: Connectionist Model Generation." In Artificial Neural Networks – ICANN 2006, 1–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_1.
Повний текст джерелаMitsuishi, Takashi, and Yasunari Shidama. "Height Defuzzification Method on L ∞ Space." In Artificial Neural Networks – ICANN 2009, 598–607. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04274-4_62.
Повний текст джерелаHuang, Haiping. "Spin Glass Models and Cavity Method." In Statistical Mechanics of Neural Networks, 5–15. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7570-6_2.
Повний текст джерелаLee, John A., and Michel Verleysen. "Nonlinear Projection with the Isotop Method." In Artificial Neural Networks — ICANN 2002, 933–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_151.
Повний текст джерелаMagoulas, G. D., M. N. Vrahatis, T. N. Grapsa, and G. S. Androulakis. "Neural Network Supervised Training Based on a Dimension Reducing Method." In Mathematics of Neural Networks, 245–49. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6099-9_41.
Повний текст джерелаTong, Zhiqiang, Kazuyuki Aihara, and Gouhei Tanaka. "A Hybrid Pooling Method for Convolutional Neural Networks." In Neural Information Processing, 454–61. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_51.
Повний текст джерелаТези доповідей конференцій з теми "Neural Networks method"
Mikaelian, Andrei L., Boris S. Kiselyov, and Nickolay Y. Kulakov. "Modification of simulated annealing method for solving combinatorial optimization problems." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983196.
Повний текст джерелаSevo, Igor. "Semi-supervised neural network training method for fast-moving object detection." In 2018 14th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018. http://dx.doi.org/10.1109/neurel.2018.8586986.
Повний текст джерелаBenmaghnia, Hanane, Matthieu Martel, and Yassamine Seladji. "Fixed-Point Code Synthesis for Neural Networks." In 6th International Conference on Artificial Intelligence, Soft Computing and Applications (AISCA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120202.
Повний текст джерелаFerreira, João, Manuel de Sousa Ribeiro, Ricardo Gonçalves, and João Leite. "Looking Inside the Black-Box: Logic-based Explanations for Neural Networks." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/45.
Повний текст джерелаYuskov, I. O., and E. P. Stroganova. "Corporative Combined Networks Investigation with Neural Networks Method." In 2022 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). IEEE, 2022. http://dx.doi.org/10.1109/synchroinfo55067.2022.9840871.
Повний текст джерелаPanasyuk, Lev M., and A. A. Forsh. "Optical information recording on vitreous semiconductors with a thermoplastic method of visualization." In Optical Memory and Neural Networks, edited by Andrei L. Mikaelian. SPIE, 1991. http://dx.doi.org/10.1117/12.50416.
Повний текст джерелаKassem, Ayman H., and Ihab G. Adam. "Optimizing Neural Networks for Leak Monitoring in Pipelines." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-3005.
Повний текст джерелаNagasawa, Yurina, Tomotaka Kimura, Takanori Kudo, and Kouji Hirata. "Estimation method of network availability with convolutional neural networks." In 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2019. http://dx.doi.org/10.1109/icce-tw46550.2019.8991804.
Повний текст джерелаKelleher, Matthew D., Thomas J. Cronley, K. T. Yang, and Mihir Sen. "Using Artificial Neural Networks to Develop a Predictive Method From Complex Experimental Heat Transfer Data." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/htd-24285.
Повний текст джерелаRangarajan, Simchony, and Chellappa. "Deterministic networks for image estimation using a penalty function method." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118495.
Повний текст джерелаЗвіти організацій з теми "Neural Networks method"
Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.
Повний текст джерелаGrossberg, Stephen. Content-Addressable Memory Storage by Neural Networks: A General Model and Global Liapunov Method,. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada192716.
Повний текст джерелаEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерелаYaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.
Повний текст джерелаKirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3743.
Повний текст джерелаSemerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.
Повний текст джерелаWarrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach, and Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, January 2009. http://dx.doi.org/10.32747/2009.7695865.bard.
Повний текст джерелаMarkova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, травень 2018. http://dx.doi.org/10.31812/0564/2250.
Повний текст джерелаSemerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and 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.
Повний текст джерелаSemerikov, Serhiy, Hanna Kucherova, Vita Los, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, April 2021. http://dx.doi.org/10.31812//123456789/4364.
Повний текст джерела