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Auxiliary models based multi-innovation gradient identification with colored measurement noises

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3 Author(s)
Feng Ding ; Sch. of Commun. & Control Eng., Jiangnan Univ., Wuxi, China ; Liu, P.X. ; Guangjun Liu

For pseudo-linear regression identification models corresponding output error systems with colored measurement noises, a difficulty of identification is that there exist unknown inner variables and unmeasurable noise terms in the information vector. This paper presents an auxiliary model based multi-innovation stochastic gradient algorithm by using the auxiliary model technique and by expanding the scalar innovation to an innovation vector. Compared with single-innovation stochastic gradient algorithm, the proposed approach can generate highly accurate parameter estimates. The simulation results confirm theoretical findings.

Published in:

Robotics and Automation, 2009. ICRA '09. IEEE International Conference on

Date of Conference:

12-17 May 2009

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