Conference proceeding
Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor
1985 24th IEEE Conference on Decision and Control, pp.1067-1071
12/1985
DOI: 10.1109/CDC.1985.268663
Abstract
Continual updating of estimates required by most recursive estimation schemes often involves redundant usage of information and may result in system instabilities in the presence of bounded output disturbances. This paper investigates an algorithm which has the capability of eliminating these difficulties. Based on a set theoretic assumption, the algorithm yields modified least-squares estimates with a forgetting factor. It updates the estimates selectively depending on whether the observed data contain sufficient information. The information evaluation required at each step involves very simple computations. In addition, the parameter estimates are shown to converge asymptotically to a region around the true parameter at an exponential rate.
Details
- Title: Subtitle
- Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor
- Creators
- S Dasgupta - University of IowaY. F Huang - University of Notre Dame
- Resource Type
- Conference proceeding
- Publication Details
- 1985 24th IEEE Conference on Decision and Control, pp.1067-1071
- Publisher
- IEEE
- DOI
- 10.1109/CDC.1985.268663
- ISSN
- 0191-2216
- Language
- English
- Date published
- 12/1985
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197166502771
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