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Relevant bibliographies by topics / Stochastic systems; neural networks; computer science

Academic literature on the topic 'Stochastic systems; neural networks; computer science'

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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Dissertations / Theses
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Journal articles on the topic "Stochastic systems; neural networks; computer science"

1

NAJIM, K., and M. CHTOUROU. "Neural networks synthesis based on stochastic approximation algorithm." International Journal of Systems Science 25, no. 7 (July 1994): 1219–22. http://dx.doi.org/10.1080/00207729408949273.

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Borkar, V. S., and P. Gupta. "Randomized neural networks for learning stochastic dependences." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 29, no. 4 (1999): 469–80. http://dx.doi.org/10.1109/3477.775263.

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Battilotti, S., and A. De Santis. "Robust output feedback control of nonlinear stochastic systems using neural networks." IEEE Transactions on Neural Networks 14, no. 1 (January 2003): 103–16. http://dx.doi.org/10.1109/tnn.2002.806609.

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Zhou, Liqun, and Guangda Hu. "Almost sure exponential stability of neutral stochastic delayed cellular neural networks." Journal of Control Theory and Applications 6, no. 2 (May 2008): 195–200. http://dx.doi.org/10.1007/s11768-008-7036-8.

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Gutiérrez, Irene López, and Christian B. Mendl. "Real time evolution with neural-network quantum states." Quantum 6 (January 20, 2022): 627. http://dx.doi.org/10.22331/q-2022-01-20-627.

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A promising application of neural-network quantum states is to describe the time dynamics of many-body quantum systems. To realize this idea, we employ neural-network quantum states to approximate the implicit midpoint rule method, which preserves the symplectic form of Hamiltonian dynamics. We ensure that our complex-valued neural networks are holomorphic functions, and exploit this property to efficiently compute gradients. Application to the transverse-field Ising model on a one- and two-dimensional lattice exhibits an accuracy comparable to the stochastic configuration method proposed in [Carleo and Troyer, Science 355, 602-606 (2017)], but does not require computing the (pseudo-)inverse of a matrix.

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Fernandes, Fernando, Rodrigo de Losso da Silveira Bueno, Pedro Delano Cavalcanti, and Alemayehu Solomon Admasu. "Generating Stochastic Processes Through Convolutional Neural Networks." Journal of Control, Automation and Electrical Systems 31, no. 2 (January 31, 2020): 294–303. http://dx.doi.org/10.1007/s40313-020-00567-y.

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7

You, Jingjing, Abdujelil Abdurahman, and Hayrengul Sadik. "Fixed/Predefined-Time Synchronization of Complex-Valued Stochastic BAM Neural Networks with Stabilizing and Destabilizing Impulse." Mathematics 10, no. 22 (November 21, 2022): 4384. http://dx.doi.org/10.3390/math10224384.

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This article is mainly concerned with the fixed-time and predefined-time synchronization problem for a type of complex-valued BAM neural networks with stochastic perturbations and impulse effect. First, some previous fixed-time stability results on nonlinear impulsive systems in which stabilizing and destabilizing impulses were separately analyzed are extended to a general case in which the stabilizing and destabilizing impulses can be handled simultaneously. Additionally, using the same logic, a new predefined-time stability lemma for stochastic nonlinear systems with a general impulsive effect is obtained by using the inequality technique. Then, based on these novel results, two novel controllers are implemented to derive some simple fixed/predefined-time synchronization criteria for the considered complex-valued impulsive BAM neural networks with stochastic perturbations using the non-separation method. Finally, two numerical examples are given to demonstrate the feasibility of the obtained results.

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Xu, Hao, and Sarangapani Jagannathan. "Neural Network-Based Finite Horizon Stochastic Optimal Control Design for Nonlinear Networked Control Systems." IEEE Transactions on Neural Networks and Learning Systems 26, no. 3 (March 2015): 472–85. http://dx.doi.org/10.1109/tnnls.2014.2315622.

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Chanthorn, Pharunyou, Grienggrai Rajchakit, Usa Humphries, Pramet Kaewmesri, Ramalingam Sriraman, and Chee Peng Lim. "A Delay-Dividing Approach to Robust Stability of Uncertain Stochastic Complex-Valued Hopfield Delayed Neural Networks." Symmetry 12, no. 5 (April 25, 2020): 683. http://dx.doi.org/10.3390/sym12050683.

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In scientific disciplines and other engineering applications, most of the systems refer to uncertainties, because when modeling physical systems the uncertain parameters are unavoidable. In view of this, it is important to investigate dynamical systems with uncertain parameters. In the present study, a delay-dividing approach is devised to study the robust stability issue of uncertain neural networks. Specifically, the uncertain stochastic complex-valued Hopfield neural network (USCVHNN) with time delay is investigated. Here, the uncertainties of the system parameters are norm-bounded. Based on the Lyapunov mathematical approach and homeomorphism principle, the sufficient conditions for the global asymptotic stability of USCVHNN are derived. To perform this derivation, we divide a complex-valued neural network (CVNN) into two parts, namely real and imaginary, using the delay-dividing approach. All the criteria are expressed by exploiting the linear matrix inequalities (LMIs). Based on two examples, we obtain good theoretical results that ascertain the usefulness of the proposed delay-dividing approach for the USCVHNN model.

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Wu, Yongbao, Jilin Zhu, and Wenxue Li. "Intermittent Discrete Observation Control for Synchronization of Stochastic Neural Networks." IEEE Transactions on Cybernetics 50, no. 6 (June 2020): 2414–24. http://dx.doi.org/10.1109/tcyb.2019.2930579.

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Dissertations / Theses on the topic "Stochastic systems; neural networks; computer science"

1

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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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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Malmgren, Henrik. "Revision of an artificial neural network enabling industrial sorting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392690.

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Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system.The findings indicate that it's important to use spatial variety dropout regularization for high resolution image inputs, and use an optimizer configuration with good convergence properties. The findings also demonstrate examples of ensemble classifiers being effectively consolidated into unified models using the distillation technique. An analogue arrangement with optimization against multiple output targets, incorporating additional information, showed accuracy gains comparable to ensembling. For use of the classifier on test data with statistics different than those of the dataset, results indicate that augmentation of the input data during classifier creation helps performance, but would, in the current case, likely need to be guided by information about the distribution shift to have sufficiently positive impact to enable a practical application. I suggest, for future development, updated architectures, automated hyperparameter search and leveraging the bountiful unlabeled data potentially available from production lines.

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Morphet, Steven Brian Işık Can. "Modeling neural networks via linguistically interpretable fuzzy inference systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2004. http://wwwlib.umi.com/cr/syr/main.

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Wheeler, Diek Winters. "Nonlinear behavior in small neural systems /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Shah, Hemal Vinodchandra 1967. "Performance evaluation of manufacturing systems using stochastic activity networks." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/278068.

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In this thesis, Stochastic Activity Networks (SANs), which are an extension to the Petri Nets, are used for performance evaluation of manufacturing systems. Using our formalism, a manufacturing system is hierarchically represented in three different layers: the manufacturing flow layer, the control layer and the network layer. SAN models are constructed for each of these layers. To simplify the understanding of the manufacturing flow, a new graphical representation, the Manufacturing Flow Network (MFN) has been developed. Conversion of MFN into SAN models simplifies the modeling of manufacturing flow layer. When MFN at the product level is very complex, a decomposition technique is applied to reduce complexity of the model under specific conditions. The accuracy of this technique is shown for specific conditions. Finally, a performance evaluation of a sample manufacturing system is shown, using the simulation for solution of the model. Performance variables of interest such as machine utilization, machine availability and operation queue length are discussed.

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Orr, Genevieve Beth. "Dynamics and algorithms for stochastic search /." Full text open access at:, 1995. http://content.ohsu.edu/u?/etd,197.

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Park, Dong Chul. "Identification of stationary/nonstationary systems using artificial neural networks /." Thesis, Connect to this title online; UW restricted, 1990. http://hdl.handle.net/1773/5822.

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林誠 and Shing Lam. "Stability of neural network control systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31214265.

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Lam, Shing. "Stability of neural network control systems /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1859797X.

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Books on the topic "Stochastic systems; neural networks; computer science"

1

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. Boston: Birkhäuser, 2010.

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2

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. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2002.

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3

H, Eeckman Frank, and Bower James M, eds. Computation and neural systems. Boston: Kluwer Academic Publishers, 1993.

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4

Stefan, Wermter, and Sun Ron 1960-, eds. Hybrid neural systems. Berlin: Springer, 2000.

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5

Cruse, Holk. Neural networks as cybernetic systems. Stuttgart: G. Thieme Verlag, 1996.

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6

Zurada, Jacek M. Introduction to artificial neural systems. St. Paul: West, 1992.

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7

Gallant, Stephen I. Neural network learning and expert systems. Cambridge, Mass: MIT Press, 1993.

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8

Medsker, L. R. Hybrid neural network and expert systems. Boston: Kluwer Academic, 1993.

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9

1959-, Yuhas Ben, and Ansari Nirwan 1958-, eds. Neural networks in telecommunications. Boston: Kluwer Academic Publishers, 1994.

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H, Eeckman Frank, and Conference on Computation and Neural Systems (1993 : Washington, D.C.), eds. Computation in neurons and neural systems. Boston: Kluwer Academic Publishers, 1994.

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Book chapters on the topic "Stochastic systems; neural networks; computer science"

1

Shen, Yi, Guoying Zhao, Minghui Jiang, and Shigeng Hu. "Stochastic High-Order Hopfield Neural Networks." In Lecture Notes in Computer Science, 740–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_98.

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2

Ritz, Raphael, and Terrence J. Sejnowski. "Correlation coding in stochastic neural networks." In Lecture Notes in Computer Science, 79–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0020136.

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Kaczorek, Tadeusz. "Neural Networks of Positive Systems." In Lecture Notes in Computer Science, 56–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24844-6_8.

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Tang, Xinhuai, and Li Xie. "Robust Exponential Stability Analysis for Uncertain Stochastic Neural Networks." In Communications in Computer and Information Science, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24022-5_1.

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Dafinca, Liliana. "Adaptive Control Systems Based on Neural Networks." In Lecture Notes in Computer Science, 615–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48774-3_66.

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Qin, Xuewen, Zaitang Huang, and Weiming Tan. "Stochastic Stability and Bifurcation Analysis on Hopfield Neural Networks with Noise." In Lecture Notes in Computer Science, 166–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15597-0_19.

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Riquelme, J., A. Gómez, J. L. Martínez, and J. A. Peças Lopes. "Overload screening of transmission systems using neural networks." In Lecture Notes in Computer Science, 796–803. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64582-9_812.

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Lyu, Xinze, Guangyao Li, Jiacheng Huang, and Wei Hu. "Rule-Guided Graph Neural Networks for Recommender Systems." In Lecture Notes in Computer Science, 384–401. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62419-4_22.

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Haitsiukevich, Katsiaryna, and Alexander Ilin. "Learning Trajectories of Hamiltonian Systems with Neural Networks." In Lecture Notes in Computer Science, 562–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_47.

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Xie, Li. "Stochastic Robust Stability Analysis for Markovian Jump Neural Networks with Time Delay." In Lecture Notes in Computer Science, 386–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_49.

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Conference papers on the topic "Stochastic systems; neural networks; computer science"

1

Zhenjiang Zhao and Qiankun Song. "New passivity result for discrete-time stochastic neural networks with time-varying delays." In 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2011. http://dx.doi.org/10.1109/csae.2011.5952778.

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Liu, Zhi chao, and Jin fang Han. "Robust Mean Square Exponential Stability of Stochastic Interval Cellular Neural Networks with Time-delays." In 2013 International Conference on Advanced Computer Science and Electronics Information. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icacsei.2013.69.

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Kurach, Karol, and Krzysztof Pawłowski. "Predicting Dangerous Seismic Activity with Recurrent Neural Networks." In 2016 Federated Conference on Computer Science and Information Systems. IEEE, 2016. http://dx.doi.org/10.15439/2016f134.

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Gajowniczek, Krzysztof, Rafik Nafkha, and Tomasz Ząbkowski. "Electricity peak demand classification with artificial neural networks." In 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017. http://dx.doi.org/10.15439/2017f168.

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Grad, Łukasz. "Helping AI to Play Hearthstone using Neural Networks." In 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017. http://dx.doi.org/10.15439/2017f561.

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Nowak, Jakub, Marcin Korytkowski, and 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.

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Kamimura, Ryotaro. "Direct Potentiality Assimilation for Improving Multi-Layered Neural Networks." In 2017 Federated Conference on Computer Science and Information Systems. PTI, 2017. http://dx.doi.org/10.15439/2017f552.

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Sartori, Camilo Chacón, and Christian Blum. "Boosting a Genetic Algorithm with Graph Neural Networks for Multi-Hop Influence Maximization in Social Networks." In 17th Conference on Computer Science and Intelligence Systems. IEEE, 2022. http://dx.doi.org/10.15439/2022f78.

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Bai, S., Q. Sun, S. Q. Li, Y. T. Li, and Y. Wang. "Applying neural networks to predict the demand of aircraft spare parts." In International Conference on Computer Science and Systems Engineering. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/csse140481.

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Maciąg, Piotr S., Wojciech Sitek, Łukasz Skonieczny, and Henryk Rybiński. "A Comparative Study of Short Text Classification with Spiking Neural Networks." In 17th Conference on Computer Science and Intelligence Systems. IEEE, 2022. http://dx.doi.org/10.15439/2022f184.

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