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Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Linear systems.'
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Ramadhan, Ayad M., and Adil K. Jabbar. "Invariable (2x2) Linear Systems." Journal of Zankoy Sulaimani - Part A 5, no. 1 (March 10, 2001): 51–56. http://dx.doi.org/10.17656/jzs.10089.
Došlý, Ondřej. "Phase matrix of linear differential systems." Časopis pro pěstování matematiky 110, no. 2 (1985): 183–92. http://dx.doi.org/10.21136/cpm.1985.108587.
Lobok, Oleksij, Boris Goncharenko, Larisa Vihrova, and Marina Sych. "Synthesis of Modal Control of Multidimensional Linear Systems Using Linear Matrix Inequalities." Collected Works of Kirovohrad National Technical University. Machinery in Agricultural Production, Industry Machine Building, Automation, no. 31 (2018): 141–50. http://dx.doi.org/10.32515/2409-9392.2018.31.141-150.
Kaczorek, Tadeusz. "Inverse systems of linear systems." Archives of Electrical Engineering 59, no. 3-4 (December 1, 2010): 203–16. http://dx.doi.org/10.2478/s10171-010-0016-x.
Inverse systems of linear systemsThe concept of inverse systems for standard and positive linear systems is introduced. Necessary and sufficient conditions for the existence of the positive inverse system for continuous-time and discrete-time linear systems are established. It is shown that: 1) The inverse system of continuous-time linear system is asymptotically stable if and only if the standard system is asymptotically stable. 2) The inverse system of discrete-time linear system is asymptotically stable if and only if the standard system is unstable. 3) The inverse system of continuous-time and discrete-time linear systems are reachable if and only if the standard systems are reachable. The considerations are illustrated by numerical examples.
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SUN, Xu-dong, and Si-zong GUO. "Linear Formed General Fuzzy Linear Systems." Systems Engineering - Theory & Practice 29, no. 9 (September 2009): 92–98. http://dx.doi.org/10.1016/s1874-8651(10)60071-3.
Broomhead, D. S., J. P. Huke, and M. R. Muldoon. "Linear Filters and Non-Linear Systems." Journal of the Royal Statistical Society: Series B (Methodological) 54, no. 2 (January 1992): 373–82. http://dx.doi.org/10.1111/j.2517-6161.1992.tb01887.x.
In this thesis we study systems with switching dynamics and we propose new mathematical tools to analyse them. We show that the postulation of a global state space structure in current frameworks is restrictive and lead to potential difficulties that limit its use for the analysis of new emerging applications. In order to overcome such shortcomings, we reformulate the foundations in the study of switched systems by developing a trajectory-based approach, where we allow the use of models that are most suitable for the analysis of a each system. These models can involve sets of higher-order differential equations whose state space does not necessarily coincide. Based on this new approach, we first study closed switched systems, and we provide sufficient conditions for stability based on LMIs using the concept of multiple higher order Lyapunov function. We also study the role of positive-realness in stability of bimodal systems and we introduce the concept of positive-real completion. Furthermore, we study open switched systems by developing a dissipativity theory. We give necessary and sufficient conditions for dissipativity in terms of LMIs constructed from the coefficient matrices of the differential equations describing the modes. The relationship between dissipativity and stability is also discussed. Finally, we study the dynamics of energy distribution networks. We develop parsimonious models that deal effectively with the variant complexity of the network and the inherent switching phenomena induced by power converters. We also present the solution to instability problems caused by devices with negative impedance characteristics such as constant power loads, using tools developed in our framework.
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Markovsky, Ivan. "Exact and approximate modeling of linear systems : a behavioral approach /." Philadelphia, Pa. : Society for Industrial and Applied Mathematics, 2006. http://www.loc.gov/catdir/enhancements/fy0708/2005057537-d.html.
Hopkins, Mark A. "Pseudo-linear identification: optimal joint parameter and state estimation of linear stochastic MIMO systems." Diss., Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/53941.
This dissertation presents a new method of simultaneous parameter and state estimation for linear, stochastic, discrete—time, multiple-input, multiple-output (MIMO) (B systems. This new method is called pseudo·Iinear identification (PLID), and extends an earlier method to the more general case where system input and output measurements are corrupted by noise. PLID can be applied to completely observable, completely controllable systems with known structure (i.e., known observability indexes) and unknown parameters. No assumptions on pole and zero locations are required; and no assumptions on relative degree are required, except that the system transfer functions must be strictly proper.
Under standard gaussian assumptions on the various noises, for time-invariant systems in the class described above, it is proved that PLID is the optimal estimator (in the mean-square·error sense) of the states and the parameters, conditioned on the output measurements. It is also proved, under a reasonable assumption of persistent excitation, that the PLID parameter estimates converge a.e. to the true parameter values of the unknown system.
For deterministic systems, it is proved that PLID exactly identifies the states and parameters in the minimum possible time, so—called deadbeat identification. The proof brings out an interesting relation between the estimate error propagation and the observability matrix of the time-varying extended system (the extended system incorporates the unknown parameters into the state vector). This relation gives rise to an intuitively
appealing notion of persistent excitation.
Some results of system identification simulations are presented. Several different cases are simulated, including a two-input, two-output system with non-minimum-phase zeros, and an unstable system. A comparison of PLID with the widely used extended Kalman filter is presented for a single-input, single·output system with near cancellation of a pole-zero pair.
Results are also presented from simulations of the adaptive control of an unstable. two-input, two-output system In these simulations, PLID is used in a se1f—tuning regulator to identify the parameters needed to compute the feedback gain matrix, and (simultaneously) to estimate the system states, for the state feedback Ph. D.
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Newsham, Samantha. "Linear systems and determinants in integrable systems." Thesis, Lancaster University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.663238.
The thesis concerns linear systems and scattering theory. In particular, it presents lineal' systems for some integrable systems and finds discrete analogues for many well known results for continuous variables. It introduces some new tools from linear systems and applies them to standard integrable systems. We begin by expressing the first Painleve equation as the compatibility condition of a certain Lax pair and introduce the Korteweg-de Vries partial differential equation. We introduce the spectral curve for algebraic families and the Toda lattice. The Fredholm determinant of a trace class Hankel integral operator gives rise to a tau function. Dyson used the tau function to solve an inverse spectral problem for Schrodinger operators. When a plane wave is subject to Schrodinger's equation and scattered by a potential u, the output is described at great distances by a scattering function. The spectral problem is to find the spectrum of Schrodinger's operator in L2 and hence the scattering function. The inverse spectral problem is to find the potential given the scattering function. The scattering and inverse scattering problems are linked by the Gelfand- Levitan equation. In this thesis, for a discrete linear system, we introduce a scattering function and Hankel matrix and a version of the Gelfand-Levitan equation for discrete linear systems. We introduce the discrete operator ∑∞/k=n AkBCAk and use it to solve the Gelfand-Levitan equation and compute Fredholm determinants of Hankel operators. We produce a discrete analogue of a calculation of Poppe giving a solution to the Korteweg-de Vries equation and via the methods of linear systems find an analogous solution in terms of Hankel matrices. We then produce a discrete analogue of the Miura transform. Thus the main new contributions of this thesis are the discrete analogues of the R operator, the Gelfand- Levitan equation, the Lyapunov equation and the Miura transform.
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Marinosson, Sigurdur Freyr. "Stability Analysis of Nonlinear Systems with Linear Programming - Stabilitätsanalyse nicht-linearer Systeme mit linearer Optimierung." Gerhard-Mercator-Universitaet Duisburg, 2002. http://www.ub.uni-duisburg.de/ETD-db/theses/available/duett-02152002-111745/.
In this thesis the stability and the region of attraction of nonlinear dynamical systems' equilibrium points are considered. Methods from linear programming are combined with theorems from the Lyapunov theory of dynamical systems to develop numerical algorithms. These algorithms deliver non-trivial information about the stability-behaviour of an equilibrium of a continuous, autonomous, nonlinear system. Two linear programs, LP1 and LP2, are developed. LP1 depends on a simply connected open neighborhood N of the equilibrium at the origin and two constants, a and m. The construction of LP1 implies that if it does not possess a feasible solution, then the corresponding system is not a,m-exponentially stable on N. LP2 has the property that every feasible solution of the linear program defines a piecewise-affine (piecewise-linear) Lyapunov function or a Lyapunov-like function V for the system.
Haddleton, Steven W. "Steady-state performance of discrete linear time-invariant systems /." Online version of thesis, 1994. http://hdl.handle.net/1850/11795.
Bhattacharyya, S. P., L. H. Keel, and D. N. Mohsenizadeh. Linear Systems. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1641-4.
Han, Xiaoying, and Peter Kloeden. "Linear Systems." In SpringerBriefs in Mathematics, 35–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61934-7_3.
Feintuch, Avraham. "Linear Systems." In Robust Control Theory in Hilbert Space, 77–86. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0591-3_5.
Kress, Rainer. "Linear Systems." In Graduate Texts in Mathematics, 5–24. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0599-9_2.
Fieguth, Paul. "Linear Systems." In An Introduction to Complex Systems, 67–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44606-6_5.
Arrowsmith, D. K., and C. M. Place. "Linear systems." In Dynamical Systems, 35–70. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2388-4_2.
Quarteroni, Alfio, and Fausto Saleri. "Linear systems." In Texts in Computational Science and Engineering, 123–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-32613-8_5.
Fuhrmann, Paul A., and Uwe Helmke. "Linear Systems." In The Mathematics of Networks of Linear Systems, 141–206. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16646-9_4.
Perko, Lawrence. "Linear Systems." In Texts in Applied Mathematics, 1–63. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0003-8_1.
D'Antona, Gabriele, Antonello Monti, and Ferdinanda Ponci. "A Decentralized State Estimator for Non-Linear Electric Power Systems." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374680.
Cohen, Leon. "Linear invariant systems." In Optical Engineering + Applications, edited by Franklin T. Luk. SPIE, 2007. http://dx.doi.org/10.1117/12.740184.
Weiss, G., and R. Rebarber. "Estimatable linear systems." In 1997 European Control Conference (ECC). IEEE, 1997. http://dx.doi.org/10.23919/ecc.1997.7082556.
"Linear dynamically varying versus jump linear systems." In Proceedings of the 1999 American Control Conference. IEEE, 1999. http://dx.doi.org/10.1109/acc.1999.786290.
Dautrebande, N., and G. Bastin. "Positive linear observers for positive linear systems." In 1999 European Control Conference (ECC). IEEE, 1999. http://dx.doi.org/10.23919/ecc.1999.7099454.
Borukhov, V., and O. Kvetko. "Applications of linear relations in linear systems theory." In The Fourth International Workshop on Multidimensional Systems - NDS 2005. IEEE, 2005. http://dx.doi.org/10.1109/nds.2005.195332.
Johnson, Timothy. "Synchronous switched linear systems." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268824.
Willems, Jan C. "Representations of linear systems." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537213.
"MOMENT-LINEAR STOCHASTIC SYSTEMS." In First International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0001143401900197.
Clotet, Josep, Josep Ferrer, and M. Dolors Magret. "Switched singular linear systems." In 2009 17th Mediterranean Conference on Control and Automation (MED). IEEE, 2009. http://dx.doi.org/10.1109/med.2009.5164733.
Sameh, Ahmed H. Solving Linear Systems on Multiprocessors. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada200741.
Author, Not Given. Feedback Systems for Linear Colliders. Office of Scientific and Technical Information (OSTI), April 1999. http://dx.doi.org/10.2172/10004.
Subasi, Yigit. Quantum Linear Systems Problem [Slides]. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1785467.
Raubenheimer, Tor. Final Focus Systems in Linear Colliders. Office of Scientific and Technical Information (OSTI), December 1998. http://dx.doi.org/10.2172/9937.
Beiu, Andrea-Claudia, Roxana-Mariana Beiu, and Valeriu Beiu. Optimal design of linear consecutive systems. Peeref, March 2023. http://dx.doi.org/10.54985/peeref.2303p3503376.
Young, D. M., and D. R. Kincaid. Linear stationary second-degree methods for the solution of large linear systems. Office of Scientific and Technical Information (OSTI), July 1990. http://dx.doi.org/10.2172/674848.
Sontag, Eduardo D. Regulation of Nonlinear and Generalized Linear Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada207725.
Merminga, N., J. Irwin, R. Helm, and R. D. Ruth. Collimation Systems for a TeV Linear Collider. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/1449133.
Morse, A. S. Adaptive Stabilization of Linear and Nonlinear Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada278270.
McKeague, Ian W., and Tiziano Tofoni. Nonparametric Estimation of Trends in Linear Stochastic Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada213741.
