Overview
In this chapter, the material overviewed in Chapter 4 is used to develop the foundations of dynamic programming on time scales. The primary results concern the derivation of the Hamilton-Jacobi-Bellman equation, the ghost in the dynamic programming machine, in this unified mathematical framework. Material in this chapter has appeared in Seiffertt & Wunsch, 2007, Seiffertt, Sanyal, & Wunsch, 2008a, Seiffertt, Sanyal, & Wunsch, 2008b, Seiffertt & Wunsch, 2008, and Seiffertt, 2009.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Seiffertt, J., Wunsch, D.C. (2010). Approximate Dynamic Programming on Time Scales. In: Unified Computational Intelligence for Complex Systems. Evolutionary Learning and Optimization, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03180-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-03180-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03179-3
Online ISBN: 978-3-642-03180-9
eBook Packages: EngineeringEngineering (R0)