On maximum likelihood identification of errors-in-variables models

G. Bottegal, R.S. Risuleo, M. Zamani, B. Ninness, H. Hjalmarsson

Research output: Contribution to journalConference articleAcademicpeer-review

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)2824-2829
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - 1 Jul 2017

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