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| Title | Adaptive non-linear time-series estimation based on hidden Markov models |
| Publication Type | Conference Paper |
| Year of Publication | 1993 |
| Authors | Krishnamurthy, V. |
| Conference Name | Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on |
| Pagination | 720 -725 vol.1 |
| Date Published | dec. |
| Keywords | adaptive nonlinear time-series estimation, ARMAX systems, finite-state Markov chain, gradient based scheme, hidden Markov models, maximum likelihood estimation, maximum-likelihood estimation schemes, ML model estimates, nonlinear systems, online estimation, parameter estimation, recursive EM algorithm, recursive expectation maximization algorithm, sequential estimation, state estimation, time series |
| 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 ldquo;online rdquo; estimation schemes: Recursive EM algorithm and a gradient based scheme |
| URL | http://dx.doi.org/10.1109/CDC.1993.325055 |
| DOI | 10.1109/CDC.1993.325055 |