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Transfer function estimation from noisy input and output data

✍ Scribed by Wei Xing Zheng

Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
114 KB
Volume
12
Category
Article
ISSN
0890-6327

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✦ Synopsis

Two new types of bias-eliminated least-squares (BELS) based algorithms are proposed for consistent identiÿcation of linear systems with noisy input and output measurements. It is shown that estimation of the noise variances can be implemented through one-dimension over-parametrization of the system transfer function. The two modiÿed BELS algorithms are attractive and meaningful in that noisy data are used directly in identiÿcation with no preÿltering and a direct estimate of system parameters is given without any parameter transformation. Simulation examples are included to demonstrate the e ectiveness of the two proposed algorithms. ?

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