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Maximum likelihood identification of noisy input–output models
- Source :
- Automatica. 43:464-472
- Publication Year :
- 2007
- Publisher :
- Elsevier BV, 2007.
-
Abstract
- This work deals with the identification of errors-in-variables models corrupted by white and uncorrelated Gaussian noises. By introducing an auxiliary process, it is possible to obtain a maximum likelihood solution of this identification problem, by means of a two-step iterative algorithm. This approach allows also to estimate, as a byproduct, the noise-free input and output sequences. Moreover, an analytic expression of the finite Cramer-Rao lower bound is derived. The method does not require any particular assumption on the input process, however, the ratio of the noise variances is assumed as known. The effectiveness of the proposed algorithm has been verified by means of Monte Carlo simulations.
- Subjects :
- Input/output
SYSTEM IDENTIFICATION
Iterative method
Monte Carlo method
System identification
INTERPOLATION
CRAMER-RAO LOWER BOUND
White noise
Parameter identification problem
symbols.namesake
Control and Systems Engineering
Gaussian noise
ERRORS-IN-VARIABLES MODELS
Statistics
MAXIMUM LIKELIHOOD IDENTIFICATION
symbols
Errors-in-variables models
Electrical and Electronic Engineering
Algorithm
Mathematics
Subjects
Details
- ISSN :
- 00051098
- Volume :
- 43
- Database :
- OpenAIRE
- Journal :
- Automatica
- Accession number :
- edsair.doi.dedup.....1c75182613d5ea0de9bf99aa50e063d8