Relevant bibliographies by topics / Stochastic neural networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Stochastic neural networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Stochastic neural networks"

1

Wong, Eugene. "Stochastic neural networks." Algorithmica 6, no. 1-6 (1991): 466–78. http://dx.doi.org/10.1007/bf01759054.

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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 (2019): 128–38. http://dx.doi.org/10.21276/ijircst.2019.7.5.1.

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Wu, 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.

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We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neura
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Gao, 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.

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Hu, Shigeng, Xiaoxin Liao, and Xuerong Mao. "Stochastic Hopfield neural networks." Journal of Physics A: Mathematical and General 36, no. 9 (2003): 2235–49. http://dx.doi.org/10.1088/0305-4470/36/9/303.

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Zhou, 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.

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The problem of stochastic synchronization of neutral-type neural networks with multidelays based onM-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional andM-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switching are stochastic synchronization. The synchronization condition is expressed as linear matrix inequality which can be easily s
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Shi, Xiang Dong. "Mean Square Asymptotic Stability of Neutral Stochastic Neutral Networks with Multiple Time-Varying Delays." Advanced Materials Research 684 (April 2013): 579–82. http://dx.doi.org/10.4028/www.scientific.net/amr.684.579.

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The paper considers the problems of almost surely asymptotic stability for neutral stochastic neural networks with multiple time-varying delays. By applying Lyapunov functional method and differential inequality techniques, new sufficient conditions ensuring the existence and almost surely asymptotic stability of neutral stochastic neural networks with multiple time-varying delays are established. The results are shown to be generalizations of some previously published results and are less conservative than existing results.
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SAKTHIVEL, 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 (2010): 1099–110. http://dx.doi.org/10.1142/s0217984910023141.

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This paper is concerned with the exponential stability of stochastic neural networks of neutral type with impulsive effects. By employing the Lyapunov functional and stochastic analysis, a new stability criterion for the stochastic neural network is derived in terms of linear matrix inequality. A numerical example is provided to show the effectiveness and applicability of the obtained result.
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Peretto, Pierre, and Jean-jacques Niez. "Stochastic Dynamics of Neural Networks." IEEE Transactions on Systems, Man, and Cybernetics 16, no. 1 (1986): 73–83. http://dx.doi.org/10.1109/tsmc.1986.289283.

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Kanarachos, Andreas E., and Kleanthis T. Geramanis. "Semi-Stochastic Complex Neural Networks." IFAC Proceedings Volumes 31, no. 12 (1998): 47–52. http://dx.doi.org/10.1016/s1474-6670(17)36040-8.

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Dissertations / Theses on the topic "Stochastic neural networks"

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Pensuwon, Wanida. "Stochastic dynamic hierarchical neural networks." Thesis, University of Hertfordshire, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366030.

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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory f
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CAMPOS, 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.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Processo 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 complex
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Ling, 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/.

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Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems’ natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in
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Zhao, 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.

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Hyland, 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.

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Todeschi, 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/.

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During the last twenty years several models have been proposed to improve the classic Black-Scholes framework for equity derivatives pricing. Recently a new model has been proposed: Local-Stochastic Volatility Model (LSV). This model considers volatility as the product between a deterministic and a stochastic term. So far, the model choice was not only driven by the capacity of capturing empirically observed market features well, but also by the computational tractability of the calibration process. This is now undergoing a big change since machine learning technologies offer new perspectives
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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.

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Chan, 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.

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Rising, 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.

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Books on the topic "Stochastic neural networks"

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Zhou, Wuneng, Jun Yang, Liuwei Zhou, and Dongbing Tong. Stability and Synchronization Control of Stochastic Neural Networks. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47833-2.

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Zhu, Q. M. Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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Thathachar, Mandayam A. L. Networks of learning automata: Techniques for online stochastic optimization. Kluwer Academic, 2003.

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Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Neural and stochastic methods in image and signal processing III: 28-29 July 1994, San Diego, California. SPIE, 1994.

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Focus, 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. International Institute for Advanced Studies in Systems Research and Cybernetics, 2002.

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Su-shing, Chen, Society of Photo-optical Instrumentation Engineers., and Society for Industrial and Applied Mathematics., eds. Neural and stochastic methods in image and signal processing: 20-23 July 1992, San Diego, California. SPIE, 1992.

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Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Neural and stochastic methods in image and signal processing II: 12-13 July 1993, San Diego, California. SPIE, 1993.

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R, Dougherty Edward, and Society of Photo-optical Instrumentation Engineers., eds. Neural, morphological, and stochastic methods in image and signal processing: 10-11 July, 1995, San Diego, California. SPIE, 1995.

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9

Su-shing, Chen, and Society of Photo-optical Instrumentation Engineers., eds. Stochastic and neural methods in signal processing, image processing, and computer vision: 24-26 July 1991, San Diego, California. SPIE, 1991.

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International 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. Birkhäuser, 2010.

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Book chapters on the topic "Stochastic neural networks"

1

Rojas, Raúl. "Stochastic Networks." In Neural Networks. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_14.

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Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. "Stochastic Neurons." In Neural Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4_4.

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Müller, Berndt, and Joachim Reinhardt. "Stochastic Neurons." In Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3_4.

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Hänggi, Martin, and George S. Moschytz. "Stochastic Optimization." In Cellular Neural Networks. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3220-7_6.

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Rennolls, Keith, Alan Soper, Phil Robbins, and Ray Guthrie. "Stochastic Neural Networks." In ICANN ’93. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_122.

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Zhang, Yumin, Lei Guo, Lingyao Wu, and Chunbo Feng. "On Stochastic Neutral Neural Networks." In Advances in Neural Networks — ISNN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427391_10.

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Siegelmann, Hava T. "Stochastic Dynamics." In Neural Networks and Analog Computation. Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-0707-8_9.

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Golea, Mostefa, Masahiro Matsuoka, and Yasubumi Sakakibara. "Stochastic simple recurrent neural networks." In Grammatical Interference: Learning Syntax from Sentences. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033360.

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Hidalgo, Jorge, Luís F. Seoane, Jesús M. Cortés, and Miguel A. Muñoz. "Stochastic Amplification in Neural Networks." In Trends in Mathematics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08138-0_9.

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Herve, 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. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7_29.

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Conference papers on the topic "Stochastic neural networks"

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Alibrandi, Umberto, Claudio Perez, and Khalid M. Mosalam. "Quantum Physics Stochastic Neural Networks (QPNN)." In 2024 8th International Conference on System Reliability and Safety (ICSRS). IEEE, 2024. https://doi.org/10.1109/icsrs63046.2024.10927527.

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Sen, Mrinmay, A. K. Qin, Gayathri C, Raghu Kishore N, Yen-Wei Chen, and Balasubramanian Raman. "SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650665.

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Zhao, J. "Stochastic connection neural networks." In 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950525.

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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.

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Chien, 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.

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Galan-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.

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Ramakrishnan, Swathika, and Dhireesha Kudithipudi. "On accelerating stochastic neural networks." In NANOCOM '17: ACM The Fourth Annual International Conference on Nanoscale Computing and Communication. ACM, 2017. http://dx.doi.org/10.1145/3109453.3123959.

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Weller, Dennis D., Nathaniel Bleier, Michael Hefenbrock, et al. "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.

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Gulshad, 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.

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Nikolic, 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.

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Reports on the topic "Stochastic neural networks"

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Fernández-Villaverde, Jesús, Galo Nuño, and Jesse Perla. Taming the curse of dimensionality: quantitative economics with deep learning. Banco de España, 2024. http://dx.doi.org/10.53479/38233.

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We argue that deep learning provides a promising approach to addressing the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges involved in solving dynamic equilibrium models, particularly the feedback loop between individual agents’ decisions and the aggregate consistency conditions required to achieve equilibrium. We then introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude wi
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Burton, Robert M., and Jr. Topics in Stochastics, Symbolic Dynamics and Neural Networks. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada336426.

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Fernández-Villaverde, Jesús, Joël Marbet, Galo Nuño, and Omar Rachedi. Inequality and the zero lower bound. Banco de España, 2024. http://dx.doi.org/10.53479/36133.

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This paper studies how household inequality shapes the effects of the zero lower bound (ZLB) on nominal interest rates on aggregate dynamics. To do so, we consider a heterogeneous agent New Keynesian (HANK) model with an occasionally binding ZLB and solve for its fully non-linear stochastic equilibrium using a novel neural network algorithm. In this setting, changes in the monetary policy stance influence households’precautionary savings by altering the frequency of ZLB events. As a result, the model features monetary policy non-neutrality in the long run. The degree of long-run non-neutrality
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