Journal ArticleDOI
Quantifying the error in estimated transfer functions with application to model order selection
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The paper concludes by showing how the obtained error bounds can be used for intelligent model order selection that takes into account both measurement noise and under-model- ing.Abstract:
Previous results on estimating errors or error bounds on identified transfer functions have relied on prior assumptions about the noise and the unmodeled dynamics. This prior information took the form of parameterized bounding functions or parameterized probability density functions, in the time or frequency domain with known parameters. It is shown that the parameters that quantify this prior information can themselves be estimated from the data using a maximum likelihood technique. This significantly reduces the prior information required to estimate transfer function error bounds. The authors illustrate the usefulness of the method with a number of simulation examples. How the obtained error bounds can be used for intelligent model-order selection that takes into account both measurement noise and under-modeling is shown. Another simulation study compares the method to Akaike's well-known FPE and AIC criteria. >read more
Citations
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References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
System identification using Laguerre models
TL;DR: It is shown that the model order can be reduced, compared to ARX (FIR, AR) modeling, by using Laguerre models, and the numerical accuracy of the corresponding linear regression estimation problem is improved by a suitable choice of the LaguERre parameter.
Journal ArticleDOI
On the value of information in system identification-Bounded noise case
Eli Fogel,Yih-Fang Huang +1 more
TL;DR: The important new feature of the proposed algorithms is their ability to ignore redundant data and the efficient data extraction property of the new algorithms is achieved with small computational effort and with improved performance when compared to the least square algorithm.
Book
Estimation, Control, and the Discrete Kalman Filter
TL;DR: In this paper, the authors present a general framework for estimating the probability distributions and densities of deterministic systems in the context of Measure Theory, which is based on the Radon-Nikodym Theorem.
Journal ArticleDOI
Asymptotic variance expressions for identified black-box transfer function models
TL;DR: The result is that the joint covariance matrix of the transfer functions from input to output and from driving white noise source to the additive output disturbance, respectively, is proportional to the inverse of the joint spectrum matrix for the input and driving noise multiplied by the spectrum of the additiveoutput noise.