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Relevant bibliographies by topics / Linear dynamical system

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Academic literature on the topic 'Linear dynamical system'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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Journal articles on the topic "Linear dynamical system"

1

Salim Youns, Anas. "STABILITY OF NON-LINEAR DYNAMICAL SYSTEM." International Journal of Advanced Research 9, no. 07 (2021): 275–83. http://dx.doi.org/10.21474/ijar01/13126.

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The mainobjective of this research is to study the stability of thenon-lineardynamical system by using the linearization technique of three dimension systems toobtain an approximate linear system and find its stability. We apply this technique to reaches to the stability of the public non linear dynamical systems of dimension. Finally, some proposed examples (example (1) and example (2)) are given to explain this technique and used the corollary.

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Rehman, Mutti-Ur, Jehad Alzabut, and Arfan Hyder. "Quadratic Stability of Non-Linear Systems Modeled with Norm Bounded Linear Differential Inclusions." Symmetry 12, no. 9 (2020): 1432. http://dx.doi.org/10.3390/sym12091432.

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In this article we present an ordinary differential equation based technique to study the quadratic stability of non-linear dynamical systems. The non-linear dynamical systems are modeled with norm bounded linear differential inclusions. The proposed methodology reformulate non-linear differential inclusion to an equivalent non-linear system. Lyapunov function demonstrate the existence of a symmetric positive definite matrix to analyze the stability of non-linear dynamical systems. The proposed method allows us to construct a system of ordinary differential equations to localize the spectrum o

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Zaksienė, Genovaitė. "The decay of mechanical oscillations in piecewise linear system." Lietuvos matematikos rinkinys 44 (December 17, 2004): 779–83. http://dx.doi.org/10.15388/lmr.2004.32264.

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The application of the dynamical dampers in the mechanical systems, when the sources of stimulation are impossible to abolish, is one of the ways to fight against the harmful vibrations. The linear dynamical damper of nonlinear systems can compensate the force of stimulation in wide diapason of frequency. The parameters of dynamical system where dynamical damper exists more effectively are determined.

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Cong, Nguyen Dinh. "Structural stability of linear random dynamical systems." Ergodic Theory and Dynamical Systems 16, no. 6 (1996): 1207–20. http://dx.doi.org/10.1017/s0143385700009998.

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AbstractIn this paper, structural stability of discrete-time linear random dynamical systems is studied. A random dynamical system is called structurally stable with respect to a random norm if it is topologically conjugate to any random dynamical system which is sufficiently close to it in this norm. We prove that a discrete-time linear random dynamical system is structurally stable with respect to its Lyapunov norms if and only if it is hyperbolic.

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Chen, Yongxin, Tryphon T. Georgiou, and Michele Pavon. "Optimal Transport Over a Linear Dynamical System." IEEE Transactions on Automatic Control 62, no. 5 (2017): 2137–52. http://dx.doi.org/10.1109/tac.2016.2602103.

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Mendelson, Kenneth S., and Frank G. Karioris. "Chaoticlike motion of a linear dynamical system." American Journal of Physics 59, no. 3 (1991): 221–24. http://dx.doi.org/10.1119/1.16566.

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Song, Yang, Chong Xiao Wang, and Wee Peng Tay. "Compressive Privacy for a Linear Dynamical System." IEEE Transactions on Information Forensics and Security 15 (2020): 895–910. http://dx.doi.org/10.1109/tifs.2019.2930366.

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Nakamura, Akira, and Nozomu Hamada. "Identification of nonlinear dynamical system by piecewise-linear system." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 74, no. 9 (1991): 102–15. http://dx.doi.org/10.1002/ecjc.4430740911.

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Hui, Qing, and Wassim M. Haddad. "Semistability of switched dynamical systems, Part I: Linear system theory." Nonlinear Analysis: Hybrid Systems 3, no. 3 (2009): 343–53. http://dx.doi.org/10.1016/j.nahs.2009.02.003.

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IMAMURA, Hitoshi. "425 Formulation of Piecewise Linear System by Integrable Dynamical System." Proceedings of the Dynamics & Design Conference 2003 (2003): _425–1_—_425–6_. http://dx.doi.org/10.1299/jsmedmc.2003._425-1_.

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Dissertations / Theses on the topic "Linear dynamical system"

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Plischke, Elmar. "Transient effects of linear dynamical systems." [S.l.] : [s.n.], 2005. http://elib.suub.uni-bremen.de/diss/docs/00010211.pdf.

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Chou, Chun Tung. "Geometry of linear systems and identification." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320010.

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Georgatzis, Konstantinos. "Dynamical probabilistic graphical models applied to physiological condition monitoring." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28838.

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Intensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task o

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Terdpravat, Attapong. "An Ab Initio Fuzzy Dynamical System Theory: Controllability and Observability." Thesis, Available online, Georgia Institute of Technology, 2004:, 2004. http://etd.gatech.edu/theses/available/etd-11112004-175731/unrestricted/Terdpravat%5Fattapong%5F200412%5Fmast.pdf.

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Thesis (M.S.)--Mechanical Engineering, Georgia Institute of Technology, 2005.<br>Esogbue, Augustine, Committee Member ; Lee, Kok-Meng, Committee Member ; Ye-Hwa Chen, Committee Chair. Includes bibliographical references.

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Souza, Júnior Amauri Holanda de. "Regional models and minimal learning machines for nonlinear dynamical system identification." reponame:Repositório Institucional da UFC, 2014. http://www.repositorio.ufc.br/handle/riufc/12481.

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SOUZA JUNIOR, A. H. Regional models and minimal learning machines for nonlinear dynamical system identification. 2014. 116 f. Tese (Doutorado em Engenharia de Teleinformática) – Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2014.<br>Submitted by Marlene Sousa (mmarlene@ufc.br) on 2015-05-26T13:38:05Z No. of bitstreams: 1 2014_dis_ahsouzajunior.pdf: 5675945 bytes, checksum: da4cd07b3287237a51c36e519d0cae14 (MD5)<br>Approved for entry into archive by Marlene Sousa(mmarlene@ufc.br) on 2015-05-27T19:40:24Z (GMT) No. of bitstreams: 1 2014_dis_ahsouzajunior.pdf: 5675945 bytes, c

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Frazier, William. "Application of Symplectic Integration on a Dynamical System." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3213.

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Molecular Dynamics (MD) is the numerical simulation of a large system of interacting molecules, and one of the key components of a MD simulation is the numerical estimation of the solutions to a system of nonlinear differential equations. Such systems are very sensitive to discretization and round-off error, and correspondingly, standard techniques such as Runge-Kutta methods can lead to poor results. However, MD systems are conservative, which means that we can use Hamiltonian mechanics and symplectic transformations (also known as canonical transformations) in analyzing and approximating sol

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Chetty, Vasu Nephi. "Theory and Applications of Network Structure of Complex Dynamical Systems." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6270.

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One of the most powerful properties of mathematical systems theory is the fact that interconnecting systems yields composites that are themselves systems. This property allows for the engineering of complex systems by aggregating simpler systems into intricate patterns. We call these interconnection patterns the "structure" of the system. Similarly, this property also enables the understanding of complex systems by decomposing them into simpler parts. We likewise call the relationship between these parts the "structure" of the system. At first glance, these may appear to represent identical vi

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Woodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.

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Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required)

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Fung, Chi Fung. "On-line dynamical system modelling using radial basis function networks in adaptive non-linear noise cancellation." Thesis, University of Sheffield, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389790.

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Gascoyne, Daniel T. "Learning and recognition by a dynamical system with a plastic velocity field." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/20101.

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Learning is a mechanism intrinsic to all sentient biological systems. Despite the diverse range of paradigms that exist, it appears that an artificial system has yet to be developed that can emulate learning with a comparable degree of accuracy or efficiency to the human brain. With the development of new approaches comes the opportunity to reduce this disparity in performance. A model presented by Janson and Marsden [arXiv:1107.0674 (2011)] (Memory foam model) redefines the critical features that an intelligent system should demonstrate. Rather than focussing on the topological constraints of

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Books on the topic "Linear dynamical system"

1

Tong, Howell. Non-linear time series: A dynamical system approach. Clarendon Press, 1993.

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Tong, Howell. Non-linear time series: A dynamical system approach. Clarendon Press, 1990.

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Hanzon, Bernard. Identifiability, recursive identification and spaces of linear dynamical systems. Stichting Mathematische Centrum, 1989.

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Jacob, Birgit. Linear Port-Hamiltonian Systems on Infinite-dimensional Spaces. Springer Basel, 2012.

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Costa, Oswaldo L. V. Continuous-Time Markov Jump Linear Systems. Springer Berlin Heidelberg, 2013.

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1950-, Mikhailov A. S., and Zannette Damiań H, eds. Emergence of dynamical order: Synchronization phenomena in complex systems. World Scientific, 2004.

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Mohammadpour, Javad. Control of Linear Parameter Varying Systems with Applications. Springer US, 2012.

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1959-, Castillo Oscar, ed. Modelling, simulation and control of non-linear dynamical systems: An intelligent approach using soft computing and fractal theory. Taylor & Francis, 2002.

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Grigoriu, Mircea D. Linear Dynamical Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64552-6.

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L, Casti J., ed. Linear dynamical systems. Academic Press, 1987.

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Book chapters on the topic "Linear dynamical system"

1

Hasegawa, Yasumichi. "System Theory of Continuous Time Linear Systems." In System Theory of Continuous Time Finite Dimensional Dynamical Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30480-5_3.

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Regularization for Linear System Identification." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_5.

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AbstractRegularization has been intensively used in statistics and numerical analysis to stabilize the solution of ill-posed inverse problems. Its use in System Identification, instead, has been less systematic until very recently. This chapter provides an overview of the main motivations for using regularization in system identification from a “classical” (Mean Square Error) statistical perspective, also discussing how structural properties of dynamical models such as stability can be controlled via regularization. A Bayesian perspective is also provided, and the language of maximum entropy p

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Scholz, Christopher H. "Brittle Tectonics: A Non-linear Dynamical System." In Extreme Environmental Events. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7695-6_2.

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Scholz, Christopher H. "Brittle Tectonics: A Non-linear Dynamical System." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_44.

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Hasegawa, Yasumichi. "System Theory of Continuous Time So-called Linear Systems." In System Theory of Continuous Time Finite Dimensional Dynamical Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30480-5_4.

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Charron-Bost, Bernadette, Matthias Függer, Jennifer L. Welch, and Josef Widder. "Full Reversal Routing as a Linear Dynamical System." In Structural Information and Communication Complexity. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22212-2_10.

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Zhu, Shaowei, and Zachary Kincaid. "Reflections on Termination of Linear Loops." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_3.

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AbstractThis paper shows how techniques for linear dynamical systems can be used to reason about the behavior of general loops. We present two main results. First, we show that every loop that can be expressed as a transition formula in linear integer arithmetic has a best model as a deterministic affine transition system. Second, we show that for any linear dynamical system f with integer eigenvalues and any integer arithmetic formula G, there is a linear integer arithmetic formula that holds exactly for the states of f for which G is eventually invariant. Combining the two, we develop a mono

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Luca, Florian, Joël Ouaknine, and James Worrell. "Algebraic Model Checking for Discrete Linear Dynamical Systems." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15839-1_1.

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AbstractModel checking infinite-state systems is one of the central challenges in automated verification. In this survey we focus on an important and fundamental subclass of infinite-state systems, namely discrete linear dynamical systems. While such systems are ubiquitous in mathematics, physics, engineering, etc., in the present context our motivation stems from their relevance to the formal analysis and verification of program loops, weighted automata, hybrid systems, and control systems, amongst many others. Our main object of study is the problem of model checking temporal properties on t

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van der Schaft, A. J. "Duality for Linear Systems: External and State Space Characterization of the Adjoint System." In Analysis of Controlled Dynamical Systems. Birkhäuser Boston, 1991. http://dx.doi.org/10.1007/978-1-4612-3214-8_35.

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Méot, François. "FFAG, Linear." In Particle Acceleration and Detection. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59979-8_11.

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AbstractThis chapter is an introduction to linear Fixed-Field Alternating Gradient (FFAG) cyclic accelerators. It begins with a brief reminder of the historical and technological context, and continues with the theoretical material needed for the simulation exercises. It relies on charged particle optics and acceleration concepts introduced in the previous synchrotron and scaling FFAG chapters and further addresses design aspects of linear FFAGs, diverse specificities of linear FFAG optics, beam dynamics in rings, serpentine acceleration. Simulations do not require specific keywords, they use

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Conference papers on the topic "Linear dynamical system"

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Wang, Li, Chao Zhang, Samson Lasaulce, Lina Bariah, and Merouane Debbah. "Goal-Oriented State Information Compression for Linear Dynamical System Control." In 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2024. http://dx.doi.org/10.1109/spawc60668.2024.10694069.

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Singh, Vimal, and Ahmed H. Tewfik. "Fast dynamic MRI using linear dynamical system model." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471762.

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Singh, Rishabh, Shujian Yu, and Jose C. Principe. "Composite Dynamic Texture Synthesis Using Hierarchical Linear Dynamical System." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054084.

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Ford, Gabriel, Timothy Hu, Donald Bucci, and Benjamin Foster. "Unknown Signal Detection In Linear Dynamical System Noise." In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019. http://dx.doi.org/10.1109/mlsp.2019.8918917.

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Coutel, S., C. H. Lamarque, and S. Pernot. "Identification Method for Both Linear and Piecewise Linear Dynamical Systems." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48625.

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Piecewise linear systems identification method is outlined in this article. Wavelets Analysis principles are widely used in this paper. Firstly, wavelets provide a very efficient mean to construct filters that are able to cut known degree polynomial terms in experimental signal. Secondly, wavelets are introduced to detect and to localize singularities in experimental signals that are characteristic of phase changes in a piecewise linear system. Eventually, we present a method to solve inverse problem that enables extracting instantaneous parameters from experimental data of physical studied sy

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Johnson, C. D. "On the stability-robustness of linear dynamical systems." In 2010 42nd Southeastern Symposium on System Theory (SSST 2010). IEEE, 2010. http://dx.doi.org/10.1109/ssst.2010.5442795.

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Chen, Yiting, Ana M. Ospina, Fabio Pasqualetti, and Emiliano Dall'Anese. "Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems." In 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023. http://dx.doi.org/10.1109/cdc49753.2023.10384181.

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Feldman, Michael. "Time-Varying and Non-Linear Dynamical System Identification Using the Hilbert Transform." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84644.

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The objective of the paper is to explain a modern Hilbert transform method for analysis and identification of mechanical non-linear vibration structures in the case of quasiperiodic signals. This special kind of periodicity arises in experimental vibration signals. The method is based on the Hilbert transform of input and output signals in a time domain to extract the instantaneous dynamic structure characteristics. The paper focuses on the dynamic analysis and identification of three groups of dynamics systems: • Forced vibrations of linear and non-linear SDOF systems excited with quasiperiod

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Liu, Xinpeng, and Xianqiang Yang. "Towards Robust Bayesian Estimation for Linear Dynamical Systems." In 2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA). IEEE, 2023. http://dx.doi.org/10.1109/cfasta57821.2023.10243200.

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Singh, Rishabh, and Jose C. Principe. "Correntropy Based Hierarchical Linear Dynamical System For Speech Recognition." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489775.

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Reports on the topic "Linear dynamical system"

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Perdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, 2021. http://dx.doi.org/10.46337/211021.

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Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and ope

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Baader, Franz, and Marcel Lippmann. Runtime Verification Using a Temporal Description Logic Revisited. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.203.

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Formulae of linear temporal logic (LTL) can be used to specify (wanted or unwanted) properties of a dynamical system. In model checking, the system’s behaviour is described by a transition system, and one needs to check whether all possible traces of this transition system satisfy the formula. In runtime verification, one observes the actual system behaviour, which at any point in time yields a finite prefix of a trace. The task is then to check whether all continuations of this prefix to a trace satisfy (violate) the formula. More precisely, one wants to construct a monitor, i.e., a finite au

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Siddiqi, Sajid M., Byron Boots, and Geoffrey J. Gordon. A Constraint Generation Approach to Learning Stable Linear Dynamical Systems. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada480921.

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Abarbanel, D. I. (Studies of non-linear dynamics of dissipative systems). Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6261505.

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Emiliano, Diaz, and Jaspreet Singh. From linear insights to systemic solutions: the future of behavioral science. Busara, 2024. https://doi.org/10.62372/cesi7494.

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Behavioral Science has traditionally focused on understanding and influencing human behavior by identifying factors driving specific and directly related decisions. This linear approach, simplifies complex scenarios into isolated variables, and has provided the foundational insights for developing targeted interventions. While this perspective has proven effective in many cases, it may only sometimes fully capture the broader context in which behaviors occur, as a linear understanding alone is insufficient to grasp the complexities of human behavior fully. It misses important considerations li

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Muelaner, Jody Emlyn. Electric Road Systems for Dynamic Charging. SAE International, 2022. http://dx.doi.org/10.4271/epr2022007.

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Electric road systems (ERS) enable dynamic charging—the most energy efficient and economical way to decarbonize road vehicles. ERS draw electrical power directly from the grid and enable vehicles with small batteries to operate without the need to stop for charging. The three main technologies (i.e., overhead catenary lines, road-bound conductive tracks, and inductive wireless systems in the road surface) are all technically proven; however, no highway system has been commercialized. Electric Road Systems for Dynamic Charging discusses the technical and economic advantages of dynamic charging

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CARNEGIE-MELLON UNIV PITTSBURGH PA. Non-Linear Dynamics and Chaotic Motions in Feedback Controlled Elastic System. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada208628.

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Perdigão, Rui A. P. Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. Meteoceanics, 2021. http://dx.doi.org/10.46337/211028.

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We hereby embark on a frontier journey articulating two of our flagship programs – “Earth System Dynamic Intelligence” and “Quantum Information Technologies in the Earth Sciences” – to take the pulse of our planet and discern its manifold complexity in a critically changing world. Going beyond the traditional stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid approaches to information and complexity, the underlying fundamental science ignites disruptive developments empowering complex problem solving across frontier natural, social and technical geoscien

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Tunc Aldemir, Don W. Miller, Brian k. Hajek, and Peng Wang. Development of a Probabilistic Technique for On-line Parameter and State Estimation in Non-linear Dynamic Systems. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/793324.

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Sokolov, Alexander. Heating System in Smart Home Infrastructure. Intellectual Archive, 2025. https://doi.org/10.32370/iaj.3261.

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This article presents a comprehensive analysis of an innovative fuel homogenization system integrated into the infrastructure of smart homes. The author explores the concept of dynamic in-line homogenization of liquid and gaseous media in thermodynamic systems, emphasizing its applicability to modern heating equipment, including diesel and gasoline engines.

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