The quantification of large SNR for MLE of ARARMAX models
Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid
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2010. 5108-5113 Papur a gyflwynwyd yn Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, Shanghai, Tsieina.
Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid
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T1 - The quantification of large SNR for MLE of ARARMAX models
AU - Zou, Y.
AU - Heath, W.P.
PY - 2010/1/29
Y1 - 2010/1/29
N2 - Maximum likelihood estimation(MLE) is widely applied in system identification because it is consistent and has excellent convergence properties. However gradient based optimization of likelihood function might end up in local convergence. It is known that for ARMAX and ARARX models, providing a large enough Signal-to-Noise-Ratio(SNR) will avoid the potential local convergence. We show the same condition can be extended to ARARMAX models in this paper. To ease the application of this condition, the exact value of such SNR needs to be quantified. Here we realize the quantification by constrained optimization.
AB - Maximum likelihood estimation(MLE) is widely applied in system identification because it is consistent and has excellent convergence properties. However gradient based optimization of likelihood function might end up in local convergence. It is known that for ARMAX and ARARX models, providing a large enough Signal-to-Noise-Ratio(SNR) will avoid the potential local convergence. We show the same condition can be extended to ARARMAX models in this paper. To ease the application of this condition, the exact value of such SNR needs to be quantified. Here we realize the quantification by constrained optimization.
U2 - 10.1109/CDC.2009.5399593
DO - 10.1109/CDC.2009.5399593
M3 - Papur
SP - 5108
EP - 5113
T2 - Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference
Y2 - 15 December 2009 through 18 December 2009
ER -