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Stochastic Control

Editor

Listed:
  • Chris John Myers

Abstract

Uncertainty presents significant challenges in the reasoning about and controlling of complex dynamical systems. To address this challenge, numerous researchers are developing improved methods for stochastic analysis. This book presents a diverse collection of some of the latest research in this important area. In particular, this book gives an overview of some of the theoretical methods and tools for stochastic analysis, and it presents the applications of these methods to problems in systems theory, science, and economics.

Suggested Citation

  • Chris John Myers (ed.), 2010. "Stochastic Control," Books, IntechOpen, number 788.
    • Handle: RePEc:ito:pbooks:788
    DOI: 10.5772/260
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    File URL: https://www.intechopen.com/books/788
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    Citations

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    Cited by:

    1. V. Gontis & A. Kononovicius, 2014. "Consentaneous agent-based and stochastic model of the financial markets," Papers 1403.1574, arXiv.org, revised Jul 2014.
    2. Aleksejus Kononovicius & Vygintas Gontis, 2012. "Three-state herding model of the financial markets," Papers 1210.1838, arXiv.org, revised Jan 2013.
    3. Aleksejus Kononovicius & Julius Ruseckas, 2014. "Nonlinear GARCH model and 1/f noise," Papers 1412.6244, arXiv.org, revised Feb 2015.
    4. Vygintas Gontis & Aleksejus Kononovicius, 2013. "Fluctuation analysis of the three agent groups herding model," Papers 1305.5958, arXiv.org.
    5. M. J. Zyskowski & M. K. Sain & R. W. Diersing, 2011. "State-Feedback, Finite-Horizon, Cost Density-Shaping Control for the Linear Quadratic Gaussian Framework," Journal of Optimization Theory and Applications, Springer, vol. 150(2), pages 251-274, August.

    More about this item

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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