EM-based identification of continuous-time ARMA Models from irregularly sampled data

• Autores: Fangwei Chen, J.C. Agüero, Marion Gilson, Hugues Garnier, Tao Liu
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 77, 2017, págs. 293-301
• Idioma: inglés
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• Resumen

  • 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.