Bibliografías temáticas / Dynamical models on networks

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Literatura académica sobre el tema "Dynamical models on networks"

Autor: Grafiati
Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Dynamical models on networks"

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Innocenti, Giacomo, and Paolo Paoletti. "Embedding dynamical networks into distributed models." Communications in Nonlinear Science and Numerical Simulation 24, no. 1-3 (2015): 21–39. http://dx.doi.org/10.1016/j.cnsns.2014.12.009.

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Piqueira, José R. C., and Felipe Barbosa Cesar. "Dynamical Models for Computer Viruses Propagation." Mathematical Problems in Engineering 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/940526.

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Nowadays, digital computer systems and networks are the main engineering tools, being used in planning, design, operation, and control of all sizes of building, transportation, machinery, business, and life maintaining devices. Consequently, computer viruses became one of the most important sources of uncertainty, contributing to decrease the reliability of vital activities. A lot of antivirus programs have been developed, but they are limited to detecting and removing infections, based on previous knowledge of the virus code. In spite of having good adaptation capability, these programs work
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Malyshev, V. A. "Networks and dynamical systems." Advances in Applied Probability 25, no. 01 (1993): 140–75. http://dx.doi.org/10.1017/s0001867800025210.

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A new approach to the problem of classification of (deflected) random walks inor Markovian models for queueing networks with identical customers is introduced. It is based on the analysis of the intrinsic dynamical system associated with the random walk. Earlier results for small dimensions are presented from this novel point of view. We give proofs of new results for higher dimensions related to the existence of a continuous invariant measure for the underlying dynamical system. Two constants are shown to be important: the free energyM< 0 corresponds to ergodicity, the Lyapounov expone
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Malyshev, V. A. "Networks and dynamical systems." Advances in Applied Probability 25, no. 1 (1993): 140–75. http://dx.doi.org/10.2307/1427500.

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A new approach to the problem of classification of (deflected) random walks in or Markovian models for queueing networks with identical customers is introduced. It is based on the analysis of the intrinsic dynamical system associated with the random walk. Earlier results for small dimensions are presented from this novel point of view. We give proofs of new results for higher dimensions related to the existence of a continuous invariant measure for the underlying dynamical system. Two constants are shown to be important: the free energy M < 0 corresponds to ergodicity, the Lyapounov exponen
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House, Thomas, and Matt J. Keeling. "Insights from unifying modern approximations to infections on networks." Journal of The Royal Society Interface 8, no. 54 (2010): 67–73. http://dx.doi.org/10.1098/rsif.2010.0179.

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Networks are increasingly central to modern science owing to their ability to conceptualize multiple interacting components of a complex system. As a specific example of this, understanding the implications of contact network structure for the transmission of infectious diseases remains a key issue in epidemiology. Three broad approaches to this problem exist: explicit simulation; derivation of exact results for special networks; and dynamical approximations. This paper focuses on the last of these approaches, and makes two main contributions. Firstly, formal mathematical links are demonstrate
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Yeung, Enoch, Jongmin Kim, Ye Yuan, Jorge Gonçalves, and Richard M. Murray. "Data-driven network models for genetic circuits from time-series data with incomplete measurements." Journal of The Royal Society Interface 18, no. 182 (2021): 20210413. http://dx.doi.org/10.1098/rsif.2021.0413.

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Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements
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CESSAC, B. "A VIEW OF NEURAL NETWORKS AS DYNAMICAL SYSTEMS." International Journal of Bifurcation and Chaos 20, no. 06 (2010): 1585–629. http://dx.doi.org/10.1142/s0218127410026721.

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We present some recent investigations resulting from the modeling of neural networks as dynamical systems, and deal with the following questions, adressed in the context of specific models. (i) Characterizing the collective dynamics; (ii) Statistical analysis of spike trains; (iii) Interplay between dynamics and network structure; (iv) Effects of synaptic plasticity.
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CAO, QI, GUILHERME RAMOS, PAUL BOGDAN, and SÉRGIO PEQUITO. "THE ACTUATION SPECTRUM OF SPATIOTEMPORAL NETWORKS WITH POWER-LAW TIME DEPENDENCIES." Advances in Complex Systems 22, no. 07n08 (2019): 1950023. http://dx.doi.org/10.1142/s0219525919500231.

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The ability to steer the state of a dynamical network towards a desired state within a time horizon is intrinsically dependent on the number of driven nodes considered, as well as the network’s topology. The trade-off between time-to-control and the minimum number of driven nodes is captured by the notion of the actuation spectrum (AS). We study the actuation spectra of a variety of artificial and real-world networked systems, modeled by fractional-order dynamics that are capable of capturing non-Markovian time properties with power-law dependencies. We find evidence that, in both types of net
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Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu. "Liquid Time-constant Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7657–66. http://dx.doi.org/10.1609/aaai.v35i9.16936.

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We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equation
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WANG, XIAO FAN. "COMPLEX NETWORKS: TOPOLOGY, DYNAMICS AND SYNCHRONIZATION." International Journal of Bifurcation and Chaos 12, no. 05 (2002): 885–916. http://dx.doi.org/10.1142/s0218127402004802.

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Dramatic advances in the field of complex networks have been witnessed in the past few years. This paper reviews some important results in this direction of rapidly evolving research, with emphasis on the relationship between the dynamics and the topology of complex networks. Basic quantities and typical examples of various complex networks are described; and main network models are introduced, including regular, random, small-world and scale-free models. The robustness of connectivity and the epidemic dynamics in complex networks are also evaluated. To that end, synchronization in various dyn
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Tesis sobre el tema "Dynamical models on networks"

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Tupikina, Liubov. "Temporal and spatial aspects of correlation networks and dynamical network models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17746.

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In der vorliegenden Arbeit untersuchte ich die komplexen Strukturen von Netzwerken, deren zeitliche Entwicklung, die Interpretationen von verschieden Netzwerk-Massen und die Klassen der Prozesse darauf. Als Erstes leitete ich Masse für die Charakterisierung der zeitlichen Entwicklung der Netzwerke her, um räumlich Veränderungsmuster zu erkennen. Als Nächstes führe ich eine neue Methode zur Konstruktion komplexer Netzwerke von Flussfeldern ein, bei welcher man das Set-up auch rein unter Berufung Berufung auf das Geschwindigkeitsfeld ändern kann. Diese Verfahren wurden für die Korrel
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DI, GANGI Domenico. "Models of dynamical networks with applications to finance." Doctoral thesis, Scuola Normale Superiore, 2022. http://hdl.handle.net/11384/112204.

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Preciado, Víctor Manuel. "Spectral analysis for stochastic models of large-scale complex dynamical networks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45873.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>Includes bibliographical references (p. 179-196).<br>Research on large-scale complex networks has important applications in diverse systems of current interest, including the Internet, the World-Wide Web, social, biological, and chemical networks. The growing availability of massive databases, computing facilities, and reliable data analysis tools has provided a powerful framework to explore structural properties of such real-world networks. However, one cannot efficiently ret
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He, Ping. "Robust synchronization of dynamical networks with delay and uncertainty :synthesis & application." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691044.

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Nath, Madhurima. "Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/86841.

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Moore and Shannon's reliability polynomial can be used as a global statistic to explore the behavior of diffusive processes on a graph dynamical system representing a finite sized interacting system. It depends on both the network topology and the dynamics of the process and gives the probability that the system has a particular desired property. Due to the complexity involved in evaluating the exact network reliability, the problem has been classified as a NP-hard problem. The estimation of the reliability polynomials for large graphs is feasible using Monte Carlo simulations. However, the nu
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McLoone, Seamus Cornelius. "Nonlinear identification using local model networks." Thesis, Queen's University Belfast, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326349.

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Gong, Xue. "Dynamical Systems in Cell Division Cycle, Winnerless Competition Models, and Tensor Approximations." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1458303716.

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Hellmann, Tim. "Stable networks in static and dynamic models of network formation." Hamburg Kovač, 2009. http://d-nb.info/1001547497/04.

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Li, Caiwei. "Dynamic scheduling of multiclass queueing networks." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/24339.

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SIRI, ENRICO. "Dynamic traffic assignment models for disrupted networks." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1091373.

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Transportation infrastructure systems are one of the cornerstones on which modern societies are founded. They allow the movement of people and goods by enabling business activities, the setting up of supply chains, and they provide access to vital resources and services. It is commonly believed that due to their vast scale and complexity, transportation systems are among the most vulnerable infrastructures in the occurrence of a disruption, i.e. an event that involves extensive damage to people or physical facilities. The growing awareness about this issue in recent years has led to a grow
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Libros sobre el tema "Dynamical models on networks"

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Traag, Vincent. Algorithms and Dynamical Models for Communities and Reputation in Social Networks. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06391-1.

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Stavros, Siokos, ed. Financial networks: Statics and dynamics. Springer-Verlag, 1997.

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Menache, Ishai. Network games: Theory, models, and dynamics. Morgan & Claypool, 2011.

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A, Arbib Michael, Amari Shun'ichi, and U.S.-Japan Seminar on Competition and Cooperation in Neural Nets (1987 : University of Southern California), eds. Dynamic interactions in neural networks: Models and data. Springer-Verlag, 1989.

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G, Chen. Fundamentals of complex networks: Models, structures, and dynamics. John Wiley & Sons Inc., 2015.

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Arbib, Michael A., and Shun-ichi Amari, eds. Dynamic Interactions in Neural Networks: Models and Data. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4536-0.

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Aldo, Romano, and SpringerLink (Online service), eds. Dynamic Learning Networks: Models and Cases in Action. Springer-Verlag US, 2009.

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M, Harris-Warrick Ronald, ed. Dynamic biological networks: The stomatogastric nervous system. MIT Press, 1992.

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Aura, Reggiani, and Nijkamp Peter, eds. Spatial dynamics, networks and modelling. Edward Elgar, 2006.

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H, Gartner Nathan, Improta Gennaro 1942-, and International Seminar on Urban Traffic Networks (2nd : 1992 : Capri, Italy), eds. Urban traffic networks: Dynamic flow modeling and control. Springer-Verlag, 1995.

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Capítulos de libros sobre el tema "Dynamical models on networks"

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Elhadj, Zeraoulia. "Robust Chaos in Neural Networks Models." In Dynamical Systems. CRC Press, 2019. http://dx.doi.org/10.1201/9780429028939-4.

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Sporns, Olaf, Giulio Tononi, and Gerald M. Edelman. "Reentry and Dynamical Interactions of Cortical Networks." In Models of Neural Networks. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-4320-5_9.

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Meng, Ziyang, Tao Yang, and Karl H. Johansson. "Networked Dynamical System Models." In Systems & Control: Foundations & Applications. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-84682-4_3.

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Boccara, Nino. "Automata Network Models of Interacting Populations." In Cellular Automata, Dynamical Systems and Neural Networks. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-1005-3_2.

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Nagurney, Anna, and Stavros Siokos. "Dynamic Imperfect Market Models." In Financial Networks. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59066-5_10.

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Nagurney, Anna, and Stavros Siokos. "Dynamic Single Country Models." In Financial Networks. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59066-5_8.

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Abou-Jaoudé, Wassim, Jérôme Feret, and Denis Thieffry. "Derivation of Qualitative Dynamical Models from Biochemical Networks." In Computational Methods in Systems Biology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23401-4_17.

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Amari, Shun-ichi. "Dynamical Stability of Formation of Cortical Maps." In Dynamic Interactions in Neural Networks: Models and Data. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4536-0_2.

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Kinzel, Wolfgang, and Manfred Opper. "Dynamics of Learning." In Models of Neural Networks. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97171-6_4.

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Addison, J. D., and B. G. Heydecker. "Traffic Models for Dynamic Assignment." In Urban Traffic Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79641-8_8.

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Actas de conferencias sobre el tema "Dynamical models on networks"

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Pasa, Luca, Alessandro Sperduti, and Peter Tino. "Linear dynamical based models for sequential domains." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966122.

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Bove, Pasquale, Alessio Micheli, Paolo Milazzo, and Marco Podda. "Prediction of Dynamical Properties of Biochemical Pathways with Graph Neural Networks." In 11th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008964700320043.

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Ouyang, Zhengyu, and Mingzhou Song. "Statistical Analysis of Discrete Dynamical System Models for Biological Networks." In 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing. IEEE, 2009. http://dx.doi.org/10.1109/ijcbs.2009.10.

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Revay, Max, Ruigang Wang, and Ian R. Manchester. "Recurrent Equilibrium Networks: Unconstrained Learning of Stable and Robust Dynamical Models." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683054.

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Wang, Bo, Sergey Nersesov, and Hashem Ashrafiuon. "Formation Control for Underactuated Surface Vessel Networks." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3178.

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Abstract Developing distributed control algorithms for multi-agent systems is difficult when each agent is modeled as a nonlinear dynamical system. Moreover, the problem becomes far more complex if the agents do not have sufficient number of actuators to track any arbitrary trajectory. In this paper, we present the first fully decentralized approach to formation control for networks of underactuated surface vessels. The vessels are modeled as three degree of freedom planar rigid bodies with two actuators. Algebraic graph theory is used to model the network as a directed graph and employing a l
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Chaouiya, Claudine, Aurelien Naldi, Elisabeth Remy, and Denis Thieffry. "Reduction of logical models of regulatory networks yields insight into dynamical properties." In Control (MSC). IEEE, 2010. http://dx.doi.org/10.1109/cca.2010.5611238.

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Moriya, Satoshi, Hideaki Yamamoto, Ayumi Hirano-Iwata, Shigeru Kubota, and Shigeo Sato. "Quantitative Analysis of Dynamical Complexity in Cultured Neuronal Network Models for Reservoir Computing Applications." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852207.

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Yang, Chun-Lin, and C. Steve Suh. "On the Dynamics of Complex Network." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71994.

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Controlling complex network systems is challenging because network systems are highly coupled by ensembles and behaving with uncertainty. A network is composed by nodes and edges. Edges serve as the connection between nodes to exchange state information and further achieve state consensus. Through edges, the dynamics of individual nodes at the local level intimately affects the network dynamics at the global level. As a following bird can occasionally lose visual contact with the target bird in a flock at any moment, the edge between two nodes in a real world network systems is not necessarily
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Dan Wang, Xiaolong Qian, and Xiaozheng Jin. "Dynamical evolution of weighted scale-free network models." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244073.

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Crippa, Paolo, Francesco Gianfelici, and Claudio Turchetti. "Information theoretical algorithm based on statistical models for blind identification of nonstationary dynamical systems." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178880.

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Informes sobre el tema "Dynamical models on networks"

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Hirsch, Morris W., Bill Baird, Walter Freeman, and Bernice Gangale. Dynamical Systems, Neural Networks and Cortical Models ASSERT 93. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada295495.

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Abarbanel, Henry, and Philip Gill. Parameter Estimation and Model Validation of Nonlinear Dynamical Networks. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1177970.

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-worl
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Saito, Kazumi. Dynamic Trust Models between Users over Social Networks. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ada636879.

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Li, Jing. Various New Statistical Models for Modeling and Change Detection in Multidimensional Dynamic Networks. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada606729.

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Thai, My. Combating Weapons of Mass Destruction: Models, Complexity, and Algorithms in Complex Dynamic and Evolving Networks. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada625120.

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Utsugi, Akio, and Motoyuki Akamatsu. Analysis of Car-Following Behavior Using Dynamic Probabilistic Models~Identification of Driving Mode Transition Using Dynamic Bayesian Networks. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0241.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Field, Richard V.,, Hamilton E. Link, Jacek Skryzalin, and Jeremy D. Wendt. A dynamic model for social networks. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1472229.

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Liu, Ernest, and Aleh Tsyvinski. Dynamical Structure and Spectral Properties of Input-Output Networks. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w28178.

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