EM-based identification of continuous-time ARMA Models from irregularly sampled data. (March 2017)

• Record Type: Journal Article
• Authors: Chen, Fengwei; Agüero, Juan C.; Gilson, Marion; Garnier, Hugues; Liu, Tao
• Is Part Of: Automatica. Volume 77(2017)
• Page Start: 293
• Page End: 301
• Publication Date: 2017-03
• Subjects: Continuous-time ARMA model -- Maximum-likelihood -- Expectation–maximization -- Irregularly sampled data; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2016.11.020
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: 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.