Title :
Some results on ergodic and adaptive control of hidden Markov models
Author :
Duncan, T.E. ; Pasik-Duncan, B. ; Stettner, L.
Author_Institution :
Dept. of Math., Kansas Univ., Lawrence, KS, USA
Abstract :
The dynamics of a discrete time, state process are assumed to depend on the current value of the state of a possibly unobserved hidden Markov model. Both the state and the hidden process are controlled with a control that depends on the available observations. An ergodic or average cost per unit time control problem is solved making some regularity assumptions. If the transition operators of the state and the hidden Markov processes depend on an unknown random variable with a known probability law, then the Bayesian adaptive control approach is used to construct an almost optimal adaptive control.
Keywords :
Bayes methods; adaptive control; control system synthesis; hidden Markov models; statistical mechanics; suboptimal control; Bayesian adaptive control; HMM; adaptive control; almost optimal adaptive control; discrete-time state process dynamics; ergodic control; probability law; transition operators; unknown random variable; unobserved hidden Markov model; Adaptive control; Bayesian methods; Costs; Equations; Hidden Markov models; Mathematics; Process control; Random variables; State-space methods; Stochastic systems;
Conference_Titel :
Decision and Control, 2002, Proceedings of the 41st IEEE Conference on
Print_ISBN :
0-7803-7516-5
DOI :
10.1109/CDC.2002.1184708