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Добірка наукової літератури з теми "Dynamical models on networks"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "Dynamical models on networks"

Innocenti, Giacomo, and Paolo Paoletti. "Embedding dynamical networks into distributed models." Communications in Nonlinear Science and Numerical Simulation 24, no. 1-3 (July 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 just as vaccines against diseases and are not able to prevent new infections based on the network state. Here, a trial on modeling computer viruses propagation dynamics relates it to other notable events occurring in the network permitting to establish preventive policies in the network management. Data from three different viruses are collected in the Internet and two different identification techniques, autoregressive and Fourier analyses, are applied showing that it is possible to forecast the dynamics of a new virus propagation by using the data collected from other viruses that formerly infected the network.

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Malyshev, V. A. "Networks and dynamical systems." Advances in Applied Probability 25, no. 01 (March 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 exponentL< 0 defines recurrence. General conjectures, examples, unsolved problems and surprising connections with ergodic theory, classical dynamical systems and their random perturbations are largely presented. A useful notion naturally arises, the so-called scaled random perturbation of a dynamical system.

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Malyshev, V. A. "Networks and dynamical systems." Advances in Applied Probability 25, no. 1 (March 1993): 140–75. http://dx.doi.org/10.2307/1427500.

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Анотація:

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 exponent L < 0 defines recurrence. General conjectures, examples, unsolved problems and surprising connections with ergodic theory, classical dynamical systems and their random perturbations are largely presented. A useful notion naturally arises, the so-called scaled random perturbation of a dynamical system.

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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 (June 10, 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 demonstrated between several prima facie unrelated dynamical approximations. And secondly, these links are used to derive two novel dynamical models for network epidemiology, which are compared against explicit stochastic simulation. The success of these new models provides improved understanding about the interaction of network structure and transmission dynamics.

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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 (September 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 of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.

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CESSAC, B. "A VIEW OF NEURAL NETWORKS AS DYNAMICAL SYSTEMS." International Journal of Bifurcation and Chaos 20, no. 06 (June 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 (November 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 networks, the actuation spectra are similar when the time-to-control is less or equal to about 1/5 of the size of the network. Nonetheless, for a time-to-control larger than the network size, the minimum number of driven nodes required to attain controllability in networks with fractional-order dynamics may still decrease in comparison with other networks with Markovian properties. These differences suggest that the minimum number of driven nodes can be used to determine the true dynamical nature of the network. Furthermore, such differences also suggest that new generative models are required to reproduce the actuation spectra of real fractional-order dynamical networks.

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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 (May 18, 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 equations, and give rise to improved performance on time-series prediction tasks. To demonstrate these properties, we first take a theoretical approach to find bounds over their dynamics, and compute their expressive power by the trajectory length measure in a latent trajectory space. We then conduct a series of time-series prediction experiments to manifest the approximation capability of Liquid Time-Constant Networks (LTCs) compared to classical and modern RNNs.

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WANG, XIAO FAN. "COMPLEX NETWORKS: TOPOLOGY, DYNAMICS AND SYNCHRONIZATION." International Journal of Bifurcation and Chaos 12, no. 05 (May 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 dynamical networks are discussed according to their regular, small-world and scale-free connections.

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Дисертації з теми "Dynamical models on networks"

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 Korrelationen skalarer Grössen, z. B. Temperatur, entwickelt, welche eine Advektions-Diﬀusions-Dynamik in der Gegenwart von Zwingen und Dissipation. Die Flussnetzwerk-Methode zur Zeitreihenanalyse konstruiert die Korrelationsmatrizen und komplexen Netzwerke. Dies ermöglicht die Charakterisierung von Transport in Flüssigkeiten, die Identiﬁkation verschiedene Misch-Regimes in dem Fluss und die Anwendung auf die Advektions-DiﬀusionsDynamik, Klimadaten und anderen Systemen, in denen Teilchentransport eine entscheidende Rolle spielen. Als Letztes, entwickelte ich ein neuartiges Heterogener Opinion Status Modell (HOpS) und Analysetechnik basiert auf Random Walks und Netzwerktopologie Theorien, um dynamischen Prozesse in Netzwerken zu studieren, wie die Verbreitung von Meinungen in sozialen Netzwerken oder Krankheiten in der Gesellschaft. Ein neues Modell heterogener Verbreitung auf einem Netzwerk wird als Beispielssystem für HOpS verwendent, um die vergleichsweise Einfachheit zu nutzen. Die Analyse eines diskreten Phasenraums des HOPS-Modells hat überraschende Eigenschaften, welches sensibel auf die Netzwerktopologie reagieren. Sie können verallgemeinert werden, um verschiedene Klassen von komplexen Netzwerken zu quantiﬁzieren, Transportphänomene zu charakterisieren und verschiedene Zeitreihen zu analysieren.
In the thesis I studied the complex architectures of networks, the network evolution in time, the interpretation of the networks measures and a particular class of processes taking place on complex networks. Firstly, I derived the measures to characterize temporal networks evolution in order to detect spatial variability patterns in evolving systems. Secondly, I introduced a novel ﬂow-network method to construct networks from ﬂows, that also allows to modify the set-up from purely relying on the velocity ﬁeld. The ﬂow-network method is developed for correlations of a scalar quantity (temperature, for example), which satisﬁes advection-diﬀusion dynamics in the presence of forcing and dissipation. This allows to characterize transport in the ﬂuids, to identify various mixing regimes in the ﬂow and to apply this method to advection-diﬀusion dynamics, data from climate and other systems, where particles transport plays a crucial role. Thirdly, I developed a novel Heterogeneous Opinion-Status model (HOpS) and analytical technique to study dynamical processes on networks. All in all, methods, derived in the thesis, allow to quantify evolution of various classes of complex systems, to get insight into physical meaning of correlation networks and analytically to analyze processes, taking place on networks.

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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.
Includes bibliographical references (p. 179-196).
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 retrieve and store the exact or full topology for many large-scale networks. As an alternative, several stochastic network models have been proposed that attempt to capture essential characteristics of such complex topologies. Network researchers then use these stochastic models to generate topologies similar to the complex network of interest and use these topologies to test, for example, the behavior of dynamical processes in the network. In general, the topological properties of a network are not directly evident in the behavior of dynamical processes running on it. On the other hand, the eigenvalue spectra of certain matricial representations of the network topology do relate quite directly to the behavior of many dynamical processes of interest, such as random walks, Markov processes, virus/rumor spreading, or synchronization of oscillators in a network. This thesis studies spectral properties of popular stochastic network models proposed in recent years. In particular, we develop several methods to determine or estimate the spectral moments of these models. We also present a variety of techniques to extract relevant spectral information from a finite sequence of spectral moments. A range of numerical examples throughout the thesis confirms the efficacy of our approach. Our ultimate objective is to use such results to understand and predict the behavior of dynamical processes taking place in large-scale networks.
by Víctor Manuel Preciado.
Ph.D.

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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 number of samples required for an accurate estimate increases with system size. Instead, an adaptive method using Bernstein polynomials as kernel density estimators proves useful. Network reliability has a wide range of applications ranging from epidemiology to statistical physics, depending on the description of the functionality. For example, it serves as a measure to study the sensitivity of the outbreak of an infectious disease on a network to the structure of the network. It can also be used to identify important dynamics-induced contagion clusters in international food trade networks. Further, it is analogous to the partition function of the Ising model which provides insights to the interpolation between the low and high temperature limits.
Ph. D.
The research presented here explores the effects of the structural properties of an interacting system on the outcomes of a diffusive process using Moore-Shannon network reliability. The network reliability is a finite degree polynomial which provides the probability of observing a certain configuration for a diffusive process on networks. Examples of such processes analyzed here are outbreak of an epidemic in a population, spread of an invasive species through international trade of commodities and spread of a perturbation in a physical system with discrete magnetic spins. Network reliability is a novel tool which can be used to compare the efficiency of network models with the observed data, to find important components of the system as well as to estimate the functions of thermodynamic state variables.

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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 growing body of literature on the topic of performance evaluation of transportation networks when affected by disruptive events, aimed at providing adequate estimations of network operability in such contexts. A peculiarity of transportation is to be a socio-technical system where the transportation supply, represented by the infrastructure and related services, interacts with the transportation demand, consisting of all those individuals who access the infrastructure at any given time. This property makes such systems inherently complex and consequently an analysis of their vulnerability in the face of disruptive events needs to be able to account for these interactions. The aim of the present thesis is therefore to develop methodologies capable of convincingly portraying the reaction of such systems in the face of disruptive events by modeling the dynamics that emerges between users and the infrastructure. It is reasonable to assume that travelers due to changed system conditions will adapt their behavior to some degree in order to mitigate the consequences of such events. In this regard, three modeling approaches are presented in this manuscript to address the need to represent this reaction phenomenon. The first approach involves the use of an inter-period traffic assignment model able to represent the evolution of users' mobility choices in a dynamic context. For each period, the users' reaction is estimated by solving an assignment model thus computing the optimal flow distribution given the current congestion conditions. User habits are taken into account by appropriately limiting the extent of flow redistributions in order to represent the gradual adaptation of the system to the new situation. Large perturbations can trigger modal shift phenomena between one transport sub-system and another. In this regard, a multi-modal multi-class scenario analysis model is then presented. Railway and road transport sub-networks are thus embedded into an extended hyper-network to model flow exchanges between this two sub-systems. Class-specific assignment models are employed to determine the choice behavior for passenger flows and freight flows. The results of these choices are then routed through the network by means of a discrete-time dynamic flow model. Finally, the idea that users' behavior may be influenced by their habits is further explored within a path-based inter-period assignment model. It is suggested that users' route choice process is not only influenced by the travel costs of available alternatives but also by users' familiarity with them. More specifically, if changing traffic conditions suddenly make a specific route disadvantageous, users will tend to prefer those that are most topologically similar to the one they are abandoning. This assumption is then investigated by demonstrating that it implies considering a rationally bounded user choice process. The steady state reached by the system as a result of the equilibration process is then detailed and a rigorous proof is provided to show that it is equivalent to a Boundedly Rational User Equilibrium. All three approaches has been successfully applied on appropriate test networks where a disruption is simulated by altering the network topology. These models can provide an important contribution to transportation network vulnerability and resilience analyses willing to take into account the interaction between the infrastructure and users.

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Книги з теми "Dynamical models on networks"

Traag, Vincent. Algorithms and Dynamical Models for Communities and Reputation in Social Networks. Cham: 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. Berlin: Springer-Verlag, 1997.

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Menache, Ishai. Network games: Theory, models, and dynamics. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): 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. New York: Springer-Verlag, 1989.

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G, Chen. Fundamentals of complex networks: Models, structures, and dynamics. Singapore: 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. New York, NY: 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. Boston, MA: Springer-Verlag US, 2009.

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

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Aura, Reggiani, and Nijkamp Peter, eds. Spatial dynamics, networks and modelling. Cheltenham, UK: 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. Berlin: Springer-Verlag, 1995.

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Частини книг з теми "Dynamical models on networks"

Elhadj, Zeraoulia. "Robust Chaos in Neural Networks Models." In Dynamical Systems, 96–116. Boca Raton, FL : CRC Press, 2019. | “A science publishers book.”: 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, 315–41. New York, NY: 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, 21–27. Cham: 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, 23–77. Dordrecht: 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, 278–94. Berlin, Heidelberg: 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, 218–49. Berlin, Heidelberg: 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, 195–207. Cham: 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, 15–34. New York, NY: 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, 149–71. Berlin, Heidelberg: 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, 213–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79641-8_8.

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Тези доповідей конференцій з теми "Dynamical models on networks"

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 leader-follower model. We take advantage of the cascade structure of the combined nonlinear kinematic and dynamic model of surface vessels and develop a reduced-order error dynamic model using a state transformation definition. The error dynamics and consequently all system states are then stabilized using sliding mode control approach. It is shown that the stabilization of the reduced-order error dynamics guarantees uniform global asymptotic stability of the closed-loop system subject to bounded uncertainties. The proposed control method can be implemented in directed time-invariant communication networks without the availability of global position measurements for any of the vehicles participating in the network. An example of a a network of five surface vessels is simulated to verify the effective performance of the proposed control approach.

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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 always intact. Contrary to common sense, these real-world networks are usually perfectly stable even when the edges between the nodes are unstable. This suggests that not only nodes are dynamical, edges are dynamical, too. Since the edges between the nodes are changing dynamically, network configuration is also dynamical. Further, edges need be defined and quantified so that the unstable connection behavior can be properly described. The paper explores the concepts of statistical mechanics and statistical entropy to address the particular need. Statistical mechanics describes the behavior of a mechanical system that has uncertain states. Statistical entropy on the other hand defines the distribution of the microstates by probability. Entropy provides a measure of the level of network integrity. With entropy, one can assign desired dynamics to the network to ensure desired network property. This work aims to construct a complex network structure model based on the edge dynamics. Coupled with node self-dynamic and consensus law, a general dynamical network model can be constructed.

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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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Звіти організацій з теми "Dynamical models on networks"

Hirsch, Morris W., Bill Baird, Walter Freeman, and Bernice Gangale. Dynamical Systems, Neural Networks and Cortical Models ASSERT 93. Fort Belvoir, VA: Defense Technical Information Center, November 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), March 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, December 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-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.

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Saito, Kazumi. Dynamic Trust Models between Users over Social Networks. Fort Belvoir, VA: Defense Technical Information Center, March 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. Fort Belvoir, VA: Defense Technical Information Center, January 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. Fort Belvoir, VA: Defense Technical Information Center, November 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. Warrendale, PA: SAE International, May 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, July 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 detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.

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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), September 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. Cambridge, MA: National Bureau of Economic Research, December 2020. http://dx.doi.org/10.3386/w28178.

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