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
Link To Document :
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