Journal article
Least squares type algorithms for identification in the presence of modeling uncertainty
IEEE transactions on automatic control, Vol.40(4), pp.756-761
04/1995
DOI: 10.1109/9.376093
Abstract
The celebrated least squares and LMS (least-mean-squares) are system identification approaches that are easily implementable, need minimal a priori assumptions, and have very nice identification properties when the uncertainty in measurements is only due to noises and not due to unmodeled behavior of the system. When there is uncertainty present due to an unmodeled part of the system as well, however, the performance of these algorithms can be poor. Here the authors propose a "modified" weighted least squares algorithm that is geared toward identification in the presence of both unmodeled dynamics and measurement disturbances. The algorithm uses very little a priori information and is easily implementable in a recursive fashion. Through an example the authors demonstrate the improved performance of the proposed approach. Motivated by a certain worst-case property of the LMS algorithm, an H/sub /spl infin// estimation algorithm is also proposed for the same objective of identification in the presence of modeling uncertainty.< >
Details
- Title: Subtitle
- Least squares type algorithms for identification in the presence of modeling uncertainty
- Creators
- Er-Wei Bai - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USAK.M Nagpal - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on automatic control, Vol.40(4), pp.756-761
- Publisher
- IEEE
- DOI
- 10.1109/9.376093
- ISSN
- 0018-9286
- eISSN
- 1558-2523
- Language
- English
- Date published
- 04/1995
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197450902771
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