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Relevant bibliographies by topics / Linear stochastic dynamic systems / Books

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Books on the topic 'Linear stochastic dynamic systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. National Aeronautics and Space Administration, 1997.

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2

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. National Aeronautics and Space Administration, 1997.

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3

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

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4

Wolfgang, Kliemann, ed. Dynamical systems and linear algebra. American Mathematical Society, 2014.

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5

Caines, Peter E. Linear Stochastic Systems. Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611974713.

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6

Lindquist, Anders, and Giorgio Picci. Linear Stochastic Systems. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45750-4.

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7

G, Chen. Linear stochastic control systems. CRC Press, 1995.

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8

Wang, Hong. Bounded Dynamic Stochastic Systems. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0481-0.

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9

Ziegler, F., and G. I. Schuëller, eds. Nonlinear Stochastic Dynamic Engineering Systems. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83334-2.

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10

Klein Haneveld, Willem K. Duality in Stochastic Linear and Dynamic Programming. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-51697-9.

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11

Davidson, James E. H. Cointegration in linear dynamic systems. London School of Economics and Political Science, 1986.

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12

Catrien C. J. H. Bijleveld. Exploratory linear dynamic systems analysis. DSWO Press, University of Leiden, 1989.

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13

Socha, Leslaw. Linearization Methods for Stochastic Dynamic Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-72997-6.

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14

Zaslavskiĭ, G. M. Chaos in dynamic systems. Harwood Academic, 1985.

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15

Angeles, Jorge. Dynamic Response of Linear Mechanical Systems. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1027-1.

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16

Hendricks, Elbert. Linear systems control: Deterministic and stochastic methods. Springer, 2008.

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17

E, Jannerup O., and Sørensen Paul H, eds. Linear systems control: Deterministic and stochastic methods. Springer, 2008.

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18

Drăgan, Vasile, Samir Aberkane, and Ioan Lucian Popa. Robust Control of Jump Linear Stochastic Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84070-8.

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19

Skelton, Robert E. Dynamic systems control: Linear systems analysis and synthesis. Wiley, 1988.

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20

Wang, Hong. Bounded Dynamic Stochastic Systems: Modelling and Control. Springer London, 2000.

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21

Hong, Wang. Bounded dynamic stochastic systems: Modelling and control. Springer, 2000.

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22

Burton, T. D. Introduction to dynamic systems analysis. McGraw-Hill, 1994.

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23

Fabio, Casciati, ed. Dynamic motion, chaotic and stochastic behaviour. Springer-Verlag, 1994.

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24

Rajendra, Singh. Non-linear dynamic analysis of geared systems. Lewis Research Center, 1990.

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25

Singh, Rajendra. Non-linear dynamic analysis of geared systems. The Ohio State University, Dept. of Mechanical Engineering, 1990.

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26

Singh, Rajendra. Non-linear dynamic analysis of geared systems. The Ohio State University, Dept. of Mechanical Engineering, 1990.

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27

Rozovskii, B. L. Stochastic Evolution Systems: Linear Theory and Applications to Non-linear Filtering. Springer Netherlands, 1990.

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28

Heidergott, Bernd, ed. Max-Plus Linear Stochastic Systems and Perturbation Analysis. Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-38995-0.

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29

Bashirov, Agamirza E. Partially observable linear systems under dependent noises. Birkhäuser Verlag, 2003.

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30

Dragan, Vasile, Toader Morozan, and Adrian-Mihail Stoica. Mathematical Methods in Robust Control of Linear Stochastic Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8663-3.

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31

Sennott, Linn I. Stochastic Dynamic Programming and the Control of Queueing Systems. John Wiley & Sons, Inc., 1998. http://dx.doi.org/10.1002/9780470317037.

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32

Sennott, Linn I. Stochastic dynamic programming and the control of queueing systems. Wiley, 1999.

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33

United States. National Aeronautics and Space Administration., ed. Modal interaction in linear dynamic systems near degenerate modes. National Aeronautics and Space Administration, 1991.

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34

Veres, Sandor M. Structure selection of stochastic dynamic systems: The information criterion approach. Gordon and Breach Science Publishers, 1990.

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35

Veres, Sándor M. Structure selection of stochastic dynamic systems: The information criterion approach. Gordon and Breach Science Publishers, 1991.

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36

Archibald, T. W. An aggregate stochastic dynamic programming model of multi-reservoir systems. University of Edinburgh, Management School, 1996.

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37

Archibald, T. W. An aggregate stochastic dynamic programming model of multiple reservoir systems. Department of Business Studies, University of Edinburgh, 1995.

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38

Socha, Leslaw. Linearization Methods for Stochastic Dynamic Systems. Springer, 2010.

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39

Socha, Leslaw. Linearization Methods for Stochastic Dynamic Systems. Springer London, Limited, 2007.

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40

Linearization Methods for Stochastic Dynamic Systems. Springer, 2008.

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41

Anishchenko, Vadim S., Vladimir Astakhov, Alexander Neiman, Tatjana Vadivasova, and Lutz Schimansky-Geier. Nonlinear Dynamics of Chaotic and Stochastic Systems. Springer, 2003.

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42

Elworthy, K. D., Y. Le Jan, and Xue-Mei Li. On the Geometry of Diffusion Operators and Stochastic Flows. Springer London, Limited, 2007.

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43

Elworthy, K. D., Y. Le Jan, and X.-M. Li. On the Geometry of Diffusion Operators and Stochastic Flows. Springer-Verlag Telos, 2000.

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44

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Why Study Nonlinear Time Series Analysis? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0001.

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Abstract:

Nonlinear Time Series Analysis (NLTS) provides a mathematically rigorous collection of techniques designed to reconstruct real-world system dynamics from time series data on a single variable or multiple causally-related variables. NLTS facilitates scientific inquiry that emphasizes strong supportive evidence, well-conducted and thorough inquiry, and realism. Data provide an essential evidentiary portal to a reality to which we have only limited access. Random-appearing data do not prove that underlying dynamic process are subject to exogenous inherently-random forces. The possibility exists that observed volatility is generated by inherently-unstable, deterministic, and nonlinear real-world dynamic systems. NLTS allows the data to speak regarding which type of system dynamics generated them. It is capable of detecting linear as well as nonlinear deterministic system dynamics, and diagnosing the presence of linear stochastic dynamics. Our objective is to use NLTS to uncover the structure best corresponding to reality whether it be linear, nonlinear, deterministic, or stochastic. Accurate diagnosis of real-world dynamics from observed data is crucial to develop valid theory, and to formulate effective public policy based on theory.

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45

Anishchenko, Vadim S., Vladimir Astakhov, Alexander Neiman, Tatjana Vadivasova, and Lutz Schimansky-Geier. Nonlinear Dynamics of Chaotic and Stochastic Systems: Tutorial and Modern Developments. Springer, 2014.

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46

Anishchenko, Vadim S., Vladimir Astakhov, Alexander Neiman, Tatjana Vadivasova, and Lutz Schimansky-Geier. Nonlinear Dynamics of Chaotic and Stochastic Systems: Tutorial and Modern Developments. Springer London, Limited, 2007.

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47

Non-Linear Partial Differential Equations, Mathematical Physics, and Stochastic Analysis: The Helge Holden Anniversary Volume. American Mathematical Society, 2018.

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48

Linear stochastic systems. Wiley, 1988.

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49

Linear Stochastic Systems. Society for Industrial and Applied Mathematics, 2018.

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50

Mha, Ho-Seong. Deterministic and stochastic behaviors of a piecewise-linear system. 1996.

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