Добірка наукової літератури з теми "Stochastic neural networks"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Stochastic neural networks".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Stochastic neural networks"
Reddy, BhanuTeja, and Usha J.C. "Prediction of Stock Market using Stochastic Neural Networks." International Journal of Innovative Research in Computer Science & Technology 7, no. 5 (September 2019): 128–38. http://dx.doi.org/10.21276/ijircst.2019.7.5.1.
Повний текст джерелаWong, Eugene. "Stochastic neural networks." Algorithmica 6, no. 1-6 (June 1991): 466–78. http://dx.doi.org/10.1007/bf01759054.
Повний текст джерелаZhou, Wuneng, Xueqing Yang, Jun Yang, and Jun Zhou. "Stochastic Synchronization of Neutral-Type Neural Networks with Multidelays Based onM-Matrix." Discrete Dynamics in Nature and Society 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/826810.
Повний текст джерелаHu, Shigeng, Xiaoxin Liao, and Xuerong Mao. "Stochastic Hopfield neural networks." Journal of Physics A: Mathematical and General 36, no. 9 (February 19, 2003): 2235–49. http://dx.doi.org/10.1088/0305-4470/36/9/303.
Повний текст джерелаGao, Zhan, Elvin Isufi, and Alejandro Ribeiro. "Stochastic Graph Neural Networks." IEEE Transactions on Signal Processing 69 (2021): 4428–43. http://dx.doi.org/10.1109/tsp.2021.3092336.
Повний текст джерелаSAKTHIVEL, RATHINASAMY, R. SAMIDURAI, and S. MARSHAL ANTHONI. "EXPONENTIAL STABILITY FOR STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPE WITH IMPULSIVE EFFECTS." Modern Physics Letters B 24, no. 11 (May 10, 2010): 1099–110. http://dx.doi.org/10.1142/s0217984910023141.
Повний текст джерелаWu, Chunmei, Junhao Hu, and Yan Li. "Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays." Discrete Dynamics in Nature and Society 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/278571.
Повний текст джерелаKanarachos, Andreas E., and Kleanthis T. Geramanis. "Semi-Stochastic Complex Neural Networks." IFAC Proceedings Volumes 31, no. 12 (June 1998): 47–52. http://dx.doi.org/10.1016/s1474-6670(17)36040-8.
Повний текст джерелаPeretto, Pierre, and Jean-jacques Niez. "Stochastic Dynamics of Neural Networks." IEEE Transactions on Systems, Man, and Cybernetics 16, no. 1 (January 1986): 73–83. http://dx.doi.org/10.1109/tsmc.1986.289283.
Повний текст джерелаHurtado, Jorge E. "Neural networks in stochastic mechanics." Archives of Computational Methods in Engineering 8, no. 3 (September 2001): 303–42. http://dx.doi.org/10.1007/bf02736646.
Повний текст джерелаДисертації з теми "Stochastic neural networks"
Pensuwon, Wanida. "Stochastic dynamic hierarchical neural networks." Thesis, University of Hertfordshire, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366030.
Повний текст джерелаCAMPOS, LUCIANA CONCEICAO DIAS. "PERIODIC STOCHASTIC MODEL BASED ON NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17076@1.
Повний текст джерелаProcesso Estocástico é um ramo da teoria da probabilidade onde se define um conjunto de modelos que permitem o estudo de problemas com componentes aleatórias. Muitos problemas reais apresentam características complexas, tais como não-linearidade e comportamento caótico, que necessitam de modelos capazes de capturar as reais características do problema para obter um tratamento apropriado. Porém, os modelos existentes ou são lineares, cuja aplicabilidade a esses problemas pode ser inadequada, ou necessitam de uma formulação complexa, onde a aplicabilidade é limitada e específica ao problema, ou dependem de suposições a priori sobre o comportamento do problema para poderem ser aplicados. Isso motivou a elaboração de um novo modelo de processo estocástico genérico, intrinsecamente não-linear, que possa ser aplicado em uma gama de problemas de fenômenos não-lineares, de comportamento altamente estocástico, e até mesmo com características periódicas. Como as redes neurais artificiais são modelos paramétricos não-lineares, simples de entendimento e implementação, capazes de capturar comportamentos de variados tipos de problemas, decidiu-se então utilizá-las como base do novo modelo proposto nessa tese, que é denominado Processo Estocástico Neural. A não-linearidade, obtida através das redes neurais desse processo estocástico, permite que se capture adequadamente o comportamento da série histórica de problemas de fenômenos não-lineares, com características altamente estocásticas e até mesmo periódicas. O objetivo é usar esse modelo para gerar séries temporais sintéticas, igualmente prováveis à série histórica, na solução desses tipos de problemas, como por exemplo os problemas que envolvem fenômenos climatológicos, econômicos, entre outros. Escolheu-se, como estudo de caso dessa tese, aplicar o modelo proposto no tratamento de afluências mensais sob o contexto do planejamento da operação do sistema hidrotérmico brasileiro. Os resultados mostraram que o Processo Estocástico Neural consegue gerar séries sintéticas com características similares às séries históricas de afluências mensais.
Stochastic Process is a branch of probability theory which defines a set of templates that allow the study of problems with random components. Many real problems exhibit complex characteristics such as nonlinearity and chaotic behavior, which require models capable of capture the real characteristics of the problem for a appropriate treatment. However, existing models have limited application to certain problems or because they are linear models (whose application gets results inconsistent or inadequate) or because they require a complex formulation or depend on a priori assumptions about the behavior of the problem, which requires a knowledge the problem at a level of detail that there is not always available. This motivated the development of a model stochastic process based on neural networks, so that is generic to be applied in a range of problems involving highly stochastic phenomena of behavior and also can be applied to phenomena that have periodic characteristics. As artificial neural networks are non-linear models, simple to understand and implementation, able to capture behaviors of varied types problems, then decided to use them as the basis of new model proposed in this thesis, which is an intrinsically non-linear model, called the Neural Stochastic Process. Through neural networks that stochastic process, can adequately capture the behavior problems of the series of phenomena with features highly stochastic and / or periodical. The goal is to use this model to generate synthetic time series, equally likely to historical series, in solution of various problems, eg problems phenomena involving climatology, economic, among others. It was chosen as a case study of this thesis, applying the model proposed in the treatment of monthly inflows in the context of operation planning of the Brazilian hydrothermal system. The Results showed that the process can Stochastic Neural generate synthetic series of similar characteristics to the historical monthly inflow series.
Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.
Повний текст джерелаLing, Hong. "Implementation of Stochastic Neural Networks for Approximating Random Processes." Master's thesis, Lincoln University. Environment, Society and Design Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20080108.124352/.
Повний текст джерелаZhao, Jieyu. "Stochastic bit stream neural networks : theory, simulations and applications." Thesis, Royal Holloway, University of London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338916.
Повний текст джерелаHyland, P. "On the implementation of neural networks using stochastic arithmetic." Thesis, Bangor University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306224.
Повний текст джерелаTodeschi, Tiziano. "Calibration of local-stochastic volatility models with neural networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23052/.
Повний текст джерела陳穎志 and Wing-chi Chan. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241475.
Повний текст джерелаChan, Wing-chi. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22925843.
Повний текст джерелаRising, Barry John Paul. "Hardware architectures for stochastic bit-stream neural networks : design and implementation." Thesis, Royal Holloway, University of London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326219.
Повний текст джерелаКниги з теми "Stochastic neural networks"
Zhou, Wuneng, Jun Yang, Liuwei Zhou, and Dongbing Tong. Stability and Synchronization Control of Stochastic Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47833-2.
Повний текст джерелаInternational Conference on Applied Stochastic Models and Data Analysis (12th : 2007 : Chania, Greece), ed. Advances in data analysis: Theory and applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. Boston: Birkhäuser, 2010.
Знайти повний текст джерелаZhu, Q. M. Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.
Знайти повний текст джерелаThathachar, Mandayam A. L. Networks of learning automata: Techniques for online stochastic optimization. Boston, MA: Kluwer Academic, 2003.
Знайти повний текст джерелаS, Sastry P., ed. Networks of learning automata: Techniques for online stochastic optimization. Boston: Kluwer Academic, 2004.
Знайти повний текст джерелаFocus, Symposium on Learning and Adaptation in Stochastic and Statistical Systems (2001 Baden-Baden Germany). Proceedings of the Focus Symposium on Learning and Adaptation in Stochastic and Statistical Systems. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2002.
Знайти повний текст джерелаCurram, Stephen. representing intelligent decision making in discrete event simulation: a stochastic neural network approach. [s.l.]: typescript, 1997.
Знайти повний текст джерелаFalmagne, Jean-Claude, David Eppstein, Christopher Doble, Dietrich Albert, and Xiangen Hu. Knowledge spaces: Applications in education. Heidelberg: Springer, 2013.
Знайти повний текст джерелаFalmagne, Jean-Claude. Knowledge Spaces: Applications in Education. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Знайти повний текст джерелаHangartner, Ricky Dale. Probabilistic computation in stochastic pulse neuromime networks. 1994.
Знайти повний текст джерелаЧастини книг з теми "Stochastic neural networks"
Rojas, Raúl. "Stochastic Networks." In Neural Networks, 371–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_14.
Повний текст джерелаMüller, Berndt, and Joachim Reinhardt. "Stochastic Neurons." In Neural Networks, 37–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3_4.
Повний текст джерелаMüller, Berndt, Joachim Reinhardt, and Michael T. Strickland. "Stochastic Neurons." In Neural Networks, 38–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4_4.
Повний текст джерелаHänggi, Martin, and George S. Moschytz. "Stochastic Optimization." In Cellular Neural Networks, 101–25. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3220-7_6.
Повний текст джерелаRennolls, Keith, Alan Soper, Phil Robbins, and Ray Guthrie. "Stochastic Neural Networks." In ICANN ’93, 481. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_122.
Повний текст джерелаZhang, Yumin, Lei Guo, Lingyao Wu, and Chunbo Feng. "On Stochastic Neutral Neural Networks." In Advances in Neural Networks — ISNN 2005, 69–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427391_10.
Повний текст джерелаSiegelmann, Hava T. "Stochastic Dynamics." In Neural Networks and Analog Computation, 121–39. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-0707-8_9.
Повний текст джерелаGolea, Mostefa, Masahiro Matsuoka, and Yasubumi Sakakibara. "Stochastic simple recurrent neural networks." In Grammatical Interference: Learning Syntax from Sentences, 262–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033360.
Повний текст джерелаHidalgo, Jorge, Luís F. Seoane, Jesús M. Cortés, and Miguel A. Muñoz. "Stochastic Amplification in Neural Networks." In Trends in Mathematics, 45–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08138-0_9.
Повний текст джерелаHerve, Thierry, Olivier Francois, and Jacques Demongeot. "Markovian spatial properties of a random field describing a stochastic neural network: Sequential or parallel implementation?" In Neural Networks, 81–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7_29.
Повний текст джерелаТези доповідей конференцій з теми "Stochastic neural networks"
Gao, Zhan, Elvin Isufi, and Alejandro Ribeiro. "Stochastic Graph Neural Networks." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054424.
Повний текст джерелаZhao, J. "Stochastic connection neural networks." In 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950525.
Повний текст джерелаChien, Jen-Tzung, and Yu-Min Huang. "Stochastic Convolutional Recurrent Networks." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206970.
Повний текст джерелаGalan-Prado, Fabio, Alejandro Moran, Joan Font, Miquel Roca, and Josep L. Rossello. "Stochastic Radial Basis Neural Networks." In 2019 29th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS). IEEE, 2019. http://dx.doi.org/10.1109/patmos.2019.8862129.
Повний текст джерелаRamakrishnan, Swathika, and Dhireesha Kudithipudi. "On accelerating stochastic neural networks." In NANOCOM '17: ACM The Fourth Annual International Conference on Nanoscale Computing and Communication. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3109453.3123959.
Повний текст джерелаWeller, Dennis D., Nathaniel Bleier, Michael Hefenbrock, Jasmin Aghassi-Hagmann, Michael Beigl, Rakesh Kumar, and Mehdi B. Tahoori. "Printed Stochastic Computing Neural Networks." In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2021. http://dx.doi.org/10.23919/date51398.2021.9474254.
Повний текст джерелаGulshad, Sadaf, Dick Sigmund, and Jong-Hwan Kim. "Learning to reproduce stochastic time series using stochastic LSTM." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965942.
Повний текст джерелаNikolic, Konstantin P., and Ivan B. Scepanovic. "Stochastic search-based neural networks learning algorithms." In 2008 9th Symposium on Neural Network Applications in Electrical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/neurel.2008.4685579.
Повний текст джерелаJuuti, Mika, Francesco Corona, and Juha Karhunen. "Stochastic Discriminant Analysis." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280609.
Повний текст джерелаKosko, B. "Stochastic competitive learning." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137718.
Повний текст джерелаЗвіти організацій з теми "Stochastic neural networks"
Burton, Robert M., and Jr. Topics in Stochastics, Symbolic Dynamics and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada336426.
Повний текст джерела