Conference proceeding
Compatibility of Stochastic and Worst-Case System Identification: Least Squares, Maximum Likelihood and General Cases
Adaptive Control, Filtering, and Signal Processing, pp.27-42
1995
Abstract
Stochastic and worst case system identification are different and are usually treated separately. We believe that under certain assumptions there exist estimates of unknown systems that are near optimal from both the stochastic and worst case points of view. This paper studies some algorithms that produce such estimates. The algorithms combine a classical least squares or maximum likelihood estimate with a projection. It is shown that the modified estimates are closer to the true system than the least squares and maximum likelihood estimates, and that they are convergent and near optimal in the worst case setting. It is also shown that these results extend to more general cases.
Details
- Title: Subtitle
- Compatibility of Stochastic and Worst-Case System Identification: Least Squares, Maximum Likelihood and General Cases
- Creators
- Er-Wei Bai - University of Iowa, Electrical and Computer EngineeringMark S Andersland - University of Iowa, Electrical and Computer Engineering
- Resource Type
- Conference proceeding
- Publication Details
- Adaptive Control, Filtering, and Signal Processing, pp.27-42
- Publisher
- New York
- Number of pages
- 27-42
- Language
- English
- Date published
- 1995
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984304536702771
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