CSpace  > 系统科学研究所
Guo Jin; Zhao Yanlong
Source Publicationsciencechinainformationscience
AbstractSystem identification with quantized observations and persistent excitations is a fundamental and difficult problem. As the first step, this paper takes the gain system for example to investigate the identification with quantized observations and bounded persistently exciting inputs. Firstly, the identification with single threshold quantization is considered. A projection recursive algorithm is proposed to estimate the unknown parameter. By use of the conditional expectation of quantized observations with respect to the estimates, the algorithm is shown to be both mean-square and almost surely convergent. The upper bound of the convergence rate is also obtained, which has the same order as the one of the optimal estimation in the case where the system output is exactly known. Secondly, for the multi-threshold quantization, the identification algorithm is similarly constructed and its asymptotic property is analyzed. Using a multi-linear transformation, the optimal scheme of quantization values and thresholds is given. A numerical example is simulated to demonstrate the effectiveness of the algorithms and the main results obtained.
Funding Project[National Natural Science Foundation of China] ; [Youth Innovation Promotion Association of Chinese Academy of Sciences]
Document Type期刊论文
Recommended Citation
GB/T 7714
Guo Jin,Zhao Yanlong. identificationofthegainsystemwithquantizedobservationsandboundedpersistentexcitations[J]. sciencechinainformationscience,2014,57(1).
APA Guo Jin,&Zhao Yanlong.(2014).identificationofthegainsystemwithquantizedobservationsandboundedpersistentexcitations.sciencechinainformationscience,57(1).
MLA Guo Jin,et al."identificationofthegainsystemwithquantizedobservationsandboundedpersistentexcitations".sciencechinainformationscience 57.1(2014).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Guo Jin]'s Articles
[Zhao Yanlong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Guo Jin]'s Articles
[Zhao Yanlong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Guo Jin]'s Articles
[Zhao Yanlong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.