Title :
Adaptive non-linear time-series estimation based on hidden Markov models
Author :
Krishnamurthy, Vikram
Author_Institution :
Dept. of Syst. Eng., Australian Nat. Univ., Canberra, ACT
Abstract :
In this paper we propose maximum-likelihood (ML) estimation schemes for the parameters and states of ARMAX systems when the input is a finite-state Markov chain. Such models have applications in econometrics, speech processing, communication systems and neuro-biological signal processing. We derive the ML model estimates using the expectation maximization (EM) algorithm. We then develop two sequential or “online” estimation schemes: Recursive EM algorithm and a gradient based scheme
Keywords :
hidden Markov models; maximum likelihood estimation; nonlinear systems; parameter estimation; state estimation; time series; ARMAX systems; ML model estimates; adaptive nonlinear time-series estimation; finite-state Markov chain; gradient based scheme; hidden Markov models; maximum-likelihood estimation schemes; online estimation; parameter estimation; recursive EM algorithm; recursive expectation maximization algorithm; sequential estimation; state estimation; Convergence; Delay; Econometrics; Equations; Hidden Markov models; Maximum likelihood estimation; Recursive estimation; Speech; State estimation; Stochastic processes;
Conference_Titel :
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-1298-8
DOI :
10.1109/CDC.1993.325055
