Bounded error parameter estimation: noise models, recursive algorithms and H/sub /spl infin// optimality

Er-Wei Bai; K.M Nagpal; R Tempo

doi:10.1109/ACC.1995.532079

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Conference proceeding

Bounded error parameter estimation: noise models, recursive algorithms and H/sub /spl infin// optimality

Er-Wei Bai, K.M Nagpal and R Tempo

Proceedings of 1995 American Control Conference - ACC'95, Vol.5, pp.3065-3069 vol.5

American Control Conference (Seattle, Washington, 06/21/1995–06/23/1995)

1995

DOI: 10.1109/ACC.1995.532079

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Abstract

The first part of the paper deals with the relationship between various noise models and the "size" of the resulting membership set. Next, we present algorithms for various commonly encountered noise models that have the following properties: 1) they are recursive and easy to implement, and 2) after a finite "learning period" yield an estimate that is guaranteed to be in the membership set. Finally, we propose algorithms that not only have nice worst-case performance characteristics similar to those of LMS and LS, but also yield estimates that are in the membership set or "close" to it.

Least squares approximation

Marine vehicles

Noise measurement

Parameter estimation

Recursive estimation

Signal to noise ratio

Yield estimation

Details

Title: Subtitle: Bounded error parameter estimation: noise models, recursive algorithms and H/sub /spl infin// optimality
Creators: Er-Wei Bai - Iowa Univ., Iowa City, IA, USA
K.M Nagpal
R Tempo
Resource Type: Conference proceeding
Publication Details: Proceedings of 1995 American Control Conference - ACC'95, Vol.5, pp.3065-3069 vol.5
Conference: American Control Conference (Seattle, Washington, 06/21/1995–06/23/1995)
DOI: 10.1109/ACC.1995.532079
Publisher: IEEE
Language: English
Date published: 1995
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984198014202771

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