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 an alpha-derivative based derivation and proof of the Hamilton-Jacobi-Bellman equation, the solution of which is the fundamental problem in the Held 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. © 2009 IEEE.
Recommended Citation
J. Seiffertt, "An Alpha Derivative Formulation of the Hamilton-Jacobi-Bellman Equation of Dynamic Programming," Proceedings of the International Joint Conference on Neural Networks, pp. 2854 - 2859, article no. 5178990, Institute of Electrical and Electronics Engineers, Nov 2009.
The definitive version is available at https://doi.org/10.1109/IJCNN.2009.5178990
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-142443553-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
18 Nov 2009
