Title
Worst-case asymptotic properties of linear algorithms for H∞ identification
Document Type
Conference Proceeding
Publication Date
12-1-1999
Abstract
This paper considers asymptotic properties for linear algorithms in H∞ identification. The divergence of linear algorithms is characterized for H∞ identification in both time and frequency domain. The sample complexity issue is also investigated. The results of this paper complement the existing results for linear algorithms in H∞ identification.
Publication Source (Journal or Book title)
Proceedings of the IEEE Conference on Decision and Control
First Page
5314
Last Page
5319
Recommended Citation
Chen, J., & Gu, G. (1999). Worst-case asymptotic properties of linear algorithms for H∞ identification. Proceedings of the IEEE Conference on Decision and Control, 5, 5314-5319. Retrieved from https://repository.lsu.edu/eecs_pubs/330