On the uniqueness of prediction error models for systems with noisy input-output data
type: Item
instance of: scholarly article
title: On the uniqueness of prediction error models for systems with noisy input-output data
zbMATH Open document ID: 0616.93074
DOI: 10.1016/0005-1098(87)90083-5
author: Petre Stoica
author: Arye Nehorai
published in: Automatica
publication date: 1987-01-01T00:00:00Z
review text: This paper addresses the uniqueness problem of the prediction error (PE) identification for a class of linear systems with noisy input and output data. Necessary and sufficient conditions are derived for the corresponding PE loss function to have (asymptotically) a unique global minimum. The results indicate that a PE algorithm may give very bad parameter estimates for systems not satisfying these conditions. Such a possibility is illustrated by a numerical example. While the PE method is used as a vehicle for illustration, the derived conditions for global uniqueness (or identifiability) apply to any consistent estimation method based on second-order data.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93C05
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 62M20
zbMATH DE Number: 3999812
zbMATH Keywords: system identification
zbMATH Keywords: global identifiability
zbMATH Keywords: noisy systems
zbMATH Keywords: prediction error
zbMATH Keywords: parameter estimates
MaRDI profile type: Publication
full work available at URL: 0005-1098(87)90083-5
OpenAlex ID: W1966915892
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cites work: Identification of stochastic linear systems in presence of input noise
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description: scientific article; zbMATH DE number 3999812