@inproceedings{49ba795cc2e649d88d6954316fdb288e,
title = "Identification of errors-in-variables models using the EM algorithm",
abstract = "In this paper, we develop a new subspace system identification algorithm for the errors-in-variables (EIV) state space model via the EM algorithm. To initialize the EM algorithm an initial estimate is obtained by the classical errors-in-variables subspace system identification method: EIV-MOESP cite\{MOESP\} and EIV-N4SID cite\{N4SID\}. The EM algorithm is an algorithm to compute the maximum value for the likelihood function that is consists of two steps; namely the E- and M-steps. The E- and M-steps in the EM algorithm are calculated by computing the conditional expectation under the assumption that the input-output data is completely observed. Numerical examples show that the EM algorithm can monotonically improve the initial estimates obtained by subspace identification methods.",
keywords = "Errors in variables identification, Filtering and smoothing, Subspace methods",
author = "Jaafar ALMutawa",
year = "2008",
doi = "10.3182/20080706-5-KR-1001.3715",
language = "English",
isbn = "9783902661005",
series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
number = "1 PART 1",
booktitle = "Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC",
edition = "1 PART 1",
}