On Identification Multivariable State-Space Systems with Unknown but Bounded Errors in Input Data

In this paper we consider one of the possible approach to identification of multivariable systems in presence of unknown but bounded errors in input data using state-space model. The ideas and approaches developed in the theory of ill—posed problems are utilized for the joint estimation and paramete...

Full description

Saved in:
Bibliographic Details
Published inIFAC Proceedings Volumes Vol. 30; no. 11; pp. 1351 - 1356
Main Authors Gubarev, Vyacheslav F., Aksenov, Nikolay N.
Format Journal Article
LanguageEnglish
Published 01.07.1997
Subjects
bounded errors
estimation
regularization
smoothing functional
state—space model
System identification
estimation
bounded errors
state—space model
System identification
smoothing functional
regularization
Online AccessGet full text

Cover

More Information
Summary:In this paper we consider one of the possible approach to identification of multivariable systems in presence of unknown but bounded errors in input data using state-space model. The ideas and approaches developed in the theory of ill—posed problems are utilized for the joint estimation and parameter identification problem. Smoothing type functional as criterion misfit of models to available data is introduced. The properties of this criterion are investigated and some algorithms based on this criterion are proposed. Modified criterion with regularization is used to avoid many difficulties connected with overparametrization and model choice.
ISSN:1474-6670
DOI:10.1016/S1474-6670(17)43030-8