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Relevant bibliographies by topics / Stochastic control theory

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Academic literature on the topic 'Stochastic control theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Stochastic control theory"

1

Rubal’skii, G. B. "Stochastic theory of inventory control." Automation and Remote Control 70, no. 12 (December 2009): 2098–108. http://dx.doi.org/10.1134/s0005117909120169.

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Fleishman, Benzion Semionovich. "Stochastic theory of community control." Ecological Modelling 39, no. 1-2 (November 1987): 121–59. http://dx.doi.org/10.1016/0304-3800(87)90017-2.

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OHSUMI, Akira. "Some Topics in Stochastic Control Theory." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1998 (May 5, 1998): 163–70. http://dx.doi.org/10.5687/sss.1998.163.

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De la Salle, S. "Stochastic optimal control theory and application." Automatica 24, no. 3 (May 1988): 425–26. http://dx.doi.org/10.1016/0005-1098(88)90086-6.

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Chou, Sidney. "Controller Tuning Based on Stochastic Control Theory." Journal of Dynamic Systems, Measurement, and Control 110, no. 1 (March 1, 1988): 100–104. http://dx.doi.org/10.1115/1.3152638.

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A practical controller tuning method is proposed for selecting controller gains in the face of design difficulties such as poor repeatability, long delay, nonlinearity, conflicting control objectives, model inaccuracy, and system complexity. Unlike many adaptive schemes striving to acquire knowledge about the system being controlled, the proposed approach is aimed at designing nonadaptive, or at best, gain scheduling controllers in a quantitative, systematic way while meeting design specifications.

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Zadeh, L. A. "Stochastic finite-state systems in control theory." Information Sciences 251 (December 2013): 1–9. http://dx.doi.org/10.1016/j.ins.2013.06.039.

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Solo, V. "Stochastic adaptive control and Martingale limit theory." IEEE Transactions on Automatic Control 35, no. 1 (1990): 66–71. http://dx.doi.org/10.1109/9.45146.

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Repperger†, D. W., and K. A. Farris. "Stochastic resonance–a nonlinear control theory interpretation." International Journal of Systems Science 41, no. 7 (July 2010): 897–907. http://dx.doi.org/10.1080/00207720903494692.

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Zhang, Weihai, Honglei Xu, Huanqing Wang, and Zhongwei Lin. "Stochastic Systems and Control: Theory and Applications." Mathematical Problems in Engineering 2017 (2017): 1–4. http://dx.doi.org/10.1155/2017/4063015.

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Tapiero, Charles S. "Applicable stochastic control: From theory to practice." European Journal of Operational Research 73, no. 2 (March 1994): 209–25. http://dx.doi.org/10.1016/0377-2217(94)90260-7.

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Dissertations / Theses on the topic "Stochastic control theory"

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Beard, Rodney. "Ito stochastic control theory, stochastic differential games and the economic theory of mobile pastoralism /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18631.pdf.

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Hunt, K. J. "Stochastic optimal control theory with application in self-tuning control." Thesis, University of Strathclyde, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382399.

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Cao, Jie. "Stochastic inventory control in dynamic environments." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011469.

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Brand, Samuel P. C. "Spatial and stochastic epidemics : theory, simulation and control." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/56738/.

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It is now widely acknowledged that spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In this work I investigate an ensemble of techniques for understanding the stochastic dynamics of spatial and discrete epidemic processes, with especial consideration given to SIR disease dynamics for the Levins-type metapopulation. I present a toolbox of techniques for the modeller of spatial epidemics. The highlight results are a novel form of moment closure derived directly from a stochastic differential representation of the epidemic, a stochastic simulation algorithm that asymptotically in system size greatly out-performs existing simulation methods for the spatial epidemic and finally a method for tackling optimal vaccination scheduling problems for controlling the spread of an invasive pathogen.

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Hao, Xiao Qi. "The main development of stochastic control problems." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691355.

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Zhou, Yulong. "Stochastic control and approximation for Boltzmann equation." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/392.

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In this thesis we study two problems concerning probability. The first is stochastic control problem, which essentially amounts to find an optimal probability in order to optimize some reward function of probability. The second is to approximate the solution of the Boltzmann equation. Thanks to conservation of mass, the solution can be regarded as a family of probability indexed by time. In the first part, we prove a dynamic programming principle for stochastic optimal control problem with expectation constraint by measurable selection approach. Since state constraint, drawdown constraint, target constraint, quantile hedging and floor constraint can all be reformulated into expectation constraint, we apply our results to prove the corresponding dynamic programming principles for these five classes of stochastic control problems in a continuous but non-Markovian setting. In order to solve the Boltzmann equation numerically, in the second part, we propose a new model equation to approximate the Boltzmann equation without angular cutoff. Here the approximate equation incorporates Boltzmann collision operator with angular cutoff and the Landau collision operator. As a first step, we prove the well-posedness theory for our approximate equation. Then in the next step, we show the error estimate between the solutions to the approximate equation and the original equation. Compared to the standard angular cutoff approximation method, our method results in higher order of accuracy.

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Damm, Tobias. "Rational matrix equations in stochastic control /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0817/2003066858-d.html.

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Zeryos, Mihail. "Bayesian pursuit analysis and singular stochastic control." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338932.

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Kabouris, John C. "Stochastic control of the activated sludge process." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/20306.

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Huang, Hui. "Optimal control of piecewise continuous stochastic processes." Bonn : [s.n.], 1989. http://catalog.hathitrust.org/api/volumes/oclc/23831217.html.

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Books on the topic "Stochastic control theory"

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Nisio, Makiko. Stochastic Control Theory. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55123-2.

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Pasik-Duncan, Bozenna, ed. Stochastic Theory and Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48022-6.

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G, Chen. Linear stochastic control systems. Boca Raton: CRC Press, 1995.

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Åström, Karl J. Introdution to stochastic control theory. Mineola, N.Y: Dover Publications, 2006.

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Duncan, T. E., and B. Pasik-Duncan, eds. Stochastic Theory and Adaptive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/bfb0113226.

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Fleming, Wendell, and Pierre-Louis Lions, eds. Stochastic Differential Systems, Stochastic Control Theory and Applications. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8762-6.

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1928-, Fleming Wendell Helms, Lions P. L, and Workshop on Stochastic Differential Systems, Stochastic Control Theory, and Applications (1986 : IMA), eds. Stochastic differential systems, stochastic control theory, and applications. New York: Springer-Verlag, 1988.

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8

R, Neck, ed. Stochastic control theory and operational research. Amsterdam: North-Holland, 1994.

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9

Stochastic optimal control: Theory and application. New York: Wiley, 1986.

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Bruno, Andò, and Graziani Salvatore, eds. Stochastic resonance: Theory and applications. Boston: Kluwer Academic Publishers, 2000.

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Book chapters on the topic "Stochastic control theory"

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Heij, Christiaan, André C.M. Ran, and Frederik van Schagen. "Stochastic Control." In Introduction to Mathematical Systems Theory, 121–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59654-5_8.

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Sengupta, Jati K., and Phillip Fanchon. "Stochastic Control Theory." In Control Theory Methods in Economics, 97–145. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6285-6_4.

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Gómez M., Guillermo L. "Stochastic control theory." In Dynamic Probabilistic Models and Social Structure, 401–19. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2524-6_9.

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van Schuppen, Jan H. "Stochastic Control Theory." In Communications and Control Engineering, 617–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66952-2_16.

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Sethi, Suresh P. "Stochastic Optimal Control." In Optimal Control Theory, 365–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98237-3_12.

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Nisio, Makiko. "Stochastic Differential Equations." In Stochastic Control Theory, 1–30. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55123-2_1.

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Nisio, Makiko. "Stochastic Differential Games." In Stochastic Control Theory, 117–51. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55123-2_4.

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Nisio, Makiko. "Stochastic Parabolic Equations." In Stochastic Control Theory, 153–207. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55123-2_5.

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Nisio, Makiko. "Optimal Control for Diffusion Processes." In Stochastic Control Theory, 31–78. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55123-2_2.

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Nisio, Makiko. "Viscosity Solutions for HJB Equations." In Stochastic Control Theory, 79–115. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55123-2_3.

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Conference papers on the topic "Stochastic control theory"

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Pestien, Victor, and William Sudderth. "Gambling theory and stochastic control." In 26th IEEE Conference on Decision and Control. IEEE, 1987. http://dx.doi.org/10.1109/cdc.1987.272879.

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Abdul Aziz, Nur Atiq Afiqah, and Hassilah Salleh. "Control theory: Deterministic versus stochastic." In PROCEEDINGS OF THE 20TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Research in Mathematical Sciences: A Catalyst for Creativity and Innovation. AIP, 2013. http://dx.doi.org/10.1063/1.4801298.

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Chen, Shuping, and Jiongmin Yong. "Control Theory, Stochastic Analysis and Applications." In Symposium on System Sciences and Control Theory. WORLD SCIENTIFIC, 1992. http://dx.doi.org/10.1142/9789814537704.

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Chen, Xinjia. "Asymptotic distribution theory for stochastic control." In Unmanned Systems Technology XXVI, edited by Paul L. Muench, Hoa G. Nguyen, and Robert Diltz. SPIE, 2024. http://dx.doi.org/10.1117/12.3013235.

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Lygeros, John. "Stochastic hybrid systems: Theory and applications." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597267.

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Zrida, Jalel, J. Birdwell, and J. B. Cockett. "Applying stochastic decision theory to estimation problems." In 26th IEEE Conference on Decision and Control. IEEE, 1987. http://dx.doi.org/10.1109/cdc.1987.272597.

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Eringis, Deividas, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Alireza Fakhrizadeh Esfahani, and Mihaly Petreczky. "PAC-Bayesian theory for stochastic LTI systems." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9682808.

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Li, Yan, and Lei Guo. "Towards a Theory of stochastic adaptive differential games." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6160768.

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Petreczky, Mihaly, and Rene Vidal. "Realization theory of stochastic jump-Markov linear systems." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434509.

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Taylor, C. J. "Robust PIP control of multivariable stochastic systems." In IEE Colloquium on Robust Control: Theory, Software and Applications. IEE, 1997. http://dx.doi.org/10.1049/ic:19971286.

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Reports on the topic "Stochastic control theory"

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Fleming, Wendell H., and Harold J. Kushner. Numerical Methods and Approximation and Modelling Problems in Stochastic Control Theory. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada218419.

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Otrok, Christopher, Huigang Chen, Alessandro Rebucci, Gianluca Benigno, and Eric R. Young. Financial Crises and Macro-Prudential Policies. Inter-American Development Bank, February 2011. http://dx.doi.org/10.18235/0011201.

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Stochastic general equilibrium models of small open economies with occasionally binding financial frictions are capable of mimicking both the business cycles and the crisis events associated with the sudden stop in access to credit markets (Mendoza, 2010). This paper studies the inefficiencies associated with borrowing decisions in a two-sector small open production economy, finding that this economy is much more likely to display under-borrowing rather than over-borrowing in normal times. As a result, macro-prudential policies (e. g, Tobin taxes or economy-wide controls on capital inflows) are costly in welfare terms. Moreover, macro-prudential policies aimed at minimizing the probability of the crisis event might be welfare-reducing in production economies. The analysis shows that there is a much larger scope for welfare gains from policy interventions during financial crises. That is to say that, ex post or crisis-management policies dominate ex ante or macro-prudential ones.

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