• DocumentCode
    2713298
  • Title

    An alpha derivative formulation of the Hamilton-Jacobi-Bellman equation Of Dynamic Programming

  • Author

    Seiffertt, John

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Missouri Univ. of Sci. & Technol., Rolla, MO, USA
  • fYear
    2009
  • fDate
    14-19 June 2009
  • Firstpage
    2854
  • Lastpage
    2859
  • Abstract
    The time scales calculus, which includes the study of the alpha derivative, is an emerging key area in mathematics. We extend this calculus to approximate dynamic programming. In particular, we investigate application of the alpha derivative, one of the fundamental dynamic derivatives of time scales. We present a alpha-derivative based derivation and proof of the Hamilton-Jacobi-Bellman equation, the solution of which is the fundamental problem in the field of dynamic programming. By drawing together the calculus of time scales and the applied area of stochastic control via approximate dynamic programming, we connect two major fields of research.
  • Keywords
    approximation theory; calculus; dynamic programming; stochastic systems; Hamilton-Jacobi-Bellman equation; alpha derivative formulation; approximate dynamic programming; mathematics; stochastic control; time scales calculus; Approximation methods; Calculus; Costs; Differential equations; Dynamic programming; Intelligent robots; Mathematics; Partial differential equations; Programmable control; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2009. IJCNN 2009. International Joint Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-3548-7
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2009.5178990
  • Filename
    5178990