Reduction of linear continuous-time multivariable systems by matching first- and second-order information

Reduction of linear continuous-time multivariable systems by matching first- and second-order information

This paper considers the approximation of stable continuous-time multivariable linear systems from a finite number of Markov parameters and second-order information indexes. It is shown that, by properly choosing these indexes, it is possible to uniquely identify an input-output model of given order...

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Veröffentlicht in:IEEE transactions on automatic control 1994-10, Vol.39 (10), p.2126-2129
Hauptverfasser: Krajewski, W., Lepschy, A., Viaro, U.
Format: Artikel
Sprache:eng
Schlagworte:
Artificial intelligence
Autocorrelation
Covariance matrix
Informatics
Linear systems
MIMO
Observability
Transfer functions
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container_title IEEE transactions on automatic control
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creator Krajewski, W.
Lepschy, A.
Viaro, U.
description This paper considers the approximation of stable continuous-time multivariable linear systems from a finite number of Markov parameters and second-order information indexes. It is shown that, by properly choosing these indexes, it is possible to uniquely identify an input-output model of given order from an equal number of first- and second-order data.< >
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subjects Artificial intelligence
Autocorrelation
Covariance matrix
Informatics
Linear systems
MIMO
Observability
Transfer functions
title Reduction of linear continuous-time multivariable systems by matching first- and second-order information
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