- Title
- EM-based identification of continuous-time ARMA models from irregularly sampled data
- Creator
- Chen, Fengwei; Agüero, Juan C.; Gilson, Marion; Garnier, Hugues; Liu, Tao
- Relation
- Automatica Vol. 77, Issue March, p. 293-301
- Publisher Link
- http://dx.doi.org/10.1016/j.automatica.2016.11.020
- Publisher
- Pergamon Press
- Resource Type
- journal article
- Date
- 2017
- Description
- In this paper we present a novel algorithm for identifying continuous-time autoregressive moving-average models utilizing irregularly sampled data. The proposed algorithm is based on the expectation–maximization algorithm and obtains maximum-likelihood estimates. The proposed algorithm shows a fast convergence rate, good robustness to initial values, and desirable estimation accuracy. Comparisons are made with other algorithms in the literature via numerical examples.
- Subject
- continuous-time ARMA model; maximum-likelihood; expectation–maximization; irregularly sampled data
- Identifier
- http://hdl.handle.net/1959.13/1387846
- Identifier
- uon:32680
- Identifier
- ISSN:0005-1098
- Language
- eng
- Reviewed
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