Academic literature on the topic 'Neural Networks method'
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Journal articles on the topic "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.
Full textLi, 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.
Full textGolubinskiy, 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.
Full textJORGENSEN, 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.
Full textPeng, 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.
Full textNeruda, 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.
Full textFeng, 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.
Full textFan, 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.
Full textShi, 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.
Full textYang, 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.
Full textDissertations / Theses on the topic "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.
Full textTypescript. 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.
Full textCUNHA, 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.
Full textCOORDENAÇÃO 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.
Full textKAIMAL, 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.
Full textSung, 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.
Full textChavali, 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.
Full textTitle 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.
Full textMohamed, 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.
Full textRowlands, 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.
Full textBooks on the topic "Neural Networks method"
Zhang, Yunong. Zhang neural networks and neural-dynamic method. Hauppauge, N.Y: Nova Science Publishers, 2009.
Find full textHarrison, 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.
Find full textAmezcua, 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.
Full textCronley, 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.
Find full textNeural networks and simulation methods. New York: M. Dekker, 1994.
Find full textSuzuki, T. Edge detection methods using neural networks. Manchester: UMIST, 1996.
Find full textKumar, Bose Deb, ed. Neural networks: Deterministic methods of analysis. London: International Thomson Computer Press, 1996.
Find full textShepherd, Adrian J. Second-Order Methods for Neural Networks. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0953-2.
Full textPabisek, Ewa. Systemy hybrydowe intergruja̜ce MES i SSN w analizie wybranych problemów mechaniki konstrukcji i materiałów. Kraków: Wydawn. Politechniki Krakowskiej, 2008.
Find full textPabisek, Ewa. Systemy hybrydowe intergruja̜ce MES i SSN w analizie wybranych problemów mechaniki konstrukcji i materiałów. Kraków: Wydawn. Politechniki Krakowskiej, 2008.
Find full textBook chapters on the topic "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.
Full textda 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.
Full textMagoulas, 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.
Full textWei, 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.
Full textBader, 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.
Full textMitsuishi, 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.
Full textHuang, 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.
Full textLee, 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.
Full textMagoulas, 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.
Full textTong, 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.
Full textConference papers on the topic "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.
Full textSevo, 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.
Full textBenmaghnia, 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.
Full textFerreira, 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.
Full textYuskov, 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.
Full textPanasyuk, 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.
Full textKassem, 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.
Full textNagasawa, 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.
Full textKelleher, 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.
Full textRangarajan, 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.
Full textReports on the topic "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.
Full textGrossberg, 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.
Full textEngel, 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.
Full textYaroshchuk, 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.
Full textKirichek, 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.
Full textSemerikov, 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.
Full textWarrick, 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.
Full textMarkova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 2018. http://dx.doi.org/10.31812/0564/2250.
Full textSemerikov, 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.
Full textSemerikov, 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.
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