Design of risk-sensitive optimal control for stochastic recurrent neural networks by using Hamilton-Jacobi-Bellman equation
Document Type
Conference Proceeding
Publication Date
1-1-2010
Abstract
This paper presents a theoretical design for the stabilization of stochastic recurrent neural networks with respect to a risk-sensitive optimality criterion. This approach is developed by using the Hamilton-Jacobi-Bellman equation, Lyapunov technique, and inverse optimality, to obtain a risk-sensitive state feedback controller, which guarantees an achievable meaningful cost for a given risk-sensitivity parameter. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach. ©2010 IEEE.
Identifier
79953141228 (Scopus)
ISBN
[9781424477456]
Publication Title
Proceedings of the IEEE Conference on Decision and Control
External Full Text Location
https://doi.org/10.1109/CDC.2010.5717009
e-ISSN
25762370
ISSN
07431546
First Page
4151
Last Page
4156
Recommended Citation
Liu, Ziqian; Ansari, Nirwan; Kotinis, Miltiadis; and Shih, Stephen C., "Design of risk-sensitive optimal control for stochastic recurrent neural networks by using Hamilton-Jacobi-Bellman equation" (2010). Faculty Publications. 6589.
https://digitalcommons.njit.edu/fac_pubs/6589
