ARMAX models constitute an excellent compromise between performance and complexity and can model in an effective way the presence of disturbances acting on the process state. These models, however, do not take into account the observation errors on the output of the process to be identified and this can be particularly important in applications like filtering and fault diagnosis. This paper concerns extended ARMAX models that consider also the presence of additive white noise on the output observation and describes an approach for their identification that takes advantage of both the errors–in–variables framework and the instrumental variable properties. The paper reports also the results of Monte Carlo simulations that underline the effectiveness of the proposed approach.

Identification of ARMAX models with additive output noise

DIVERSI, ROBERTO;GUIDORZI, ROBERTO;SOVERINI, UMBERTO
2009

Abstract

ARMAX models constitute an excellent compromise between performance and complexity and can model in an effective way the presence of disturbances acting on the process state. These models, however, do not take into account the observation errors on the output of the process to be identified and this can be particularly important in applications like filtering and fault diagnosis. This paper concerns extended ARMAX models that consider also the presence of additive white noise on the output observation and describes an approach for their identification that takes advantage of both the errors–in–variables framework and the instrumental variable properties. The paper reports also the results of Monte Carlo simulations that underline the effectiveness of the proposed approach.
Proceedings of the 15th IFAC Symposium on System Identification
1574
1579
R. Diversi; R. Guidorzi; U. Soverini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/79748
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