Combined estimation and control of HMMs

Author

Frankpitt, Bernard ; Baras, John S.

Author_Institution

Dept. of Electr. Eng., Maryland Univ., College Park, MD, USA

Volume

3

fYear

1997

fDate

10-12 Dec 1997

Firstpage

2254

Abstract

The principal contribution of this paper is the presentation of the potential theoretical results that are needed for an application of stochastic approximation theory to the problem of demonstrating asymptotic stability for combined estimation and control of a plant described by a hidden Markov model. We motivate the results by briefly describing a combined estimation and control problem. We show how the problem translates to the stochastic approximation framework. We also show how the Markov chain that underlies the stochastic approximation problem can be decomposed into factors with discrete and continuous range. Finally, we use this decomposition to develop the results that are needed for an application of the ODE method to the stochastic control problem

Keywords

approximation theory; asymptotic stability; differential equations; hidden Markov models; recursive estimation; stochastic systems; HMM control; HMM estimation; ODE method; asymptotic stability; hidden Markov model; stochastic approximation theory; stochastic control problem; Convergence; Cost function; Educational institutions; Hidden Markov models; History; Output feedback; Recursive estimation; State estimation; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.657108

Filename

657108