Among the algorithms of linear models identification from input/output data, the N4SID (Numerical Subspace State Space System IDentiflcation) plays an important role due to its simplicity and effectiveness. It is known that N4SID gives good results for system identification in a Gaussian setting. This paper presents a technique that improves the performances of the N4SID in the case of a non-Gaussian data set The approach here followed is in the framework of polynomial estimation theory, developed in recent years, which is a simple and effective tool for the processing of non-Gaussian data.

Polynomial extension of linear subspace algorithms for stochastic identification

GERMANI, Alfredo;MANES, COSTANZO
2004-01-01

Abstract

Among the algorithms of linear models identification from input/output data, the N4SID (Numerical Subspace State Space System IDentiflcation) plays an important role due to its simplicity and effectiveness. It is known that N4SID gives good results for system identification in a Gaussian setting. This paper presents a technique that improves the performances of the N4SID in the case of a non-Gaussian data set The approach here followed is in the framework of polynomial estimation theory, developed in recent years, which is a simple and effective tool for the processing of non-Gaussian data.
978-078038682-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/42776
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