FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs

doi:10.1007/s11424-017-5305-7

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	FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs
	He, Yanyu 1; Guo, Jin 2
	2017-10-01
发表期刊	JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
ISSN	1009-6124
卷号	30 期号:5 页码:1061-1071
摘要	This paper investigates the FIR systems identification with quantized output observations and a large class of quantized inputs. The limit inferior of the regressors' frequencies of occurrences is employed to characterize the input's persistent excitation, under which the strong convergence and the convergence rate of the two-step estimation algorithm are given. As for the asymptotical efficiency, with a suitable selection of the weighting matrix in the algorithm, even though the limit of the product of the Cram,r-Rao (CR) lower bound and the data length does not exist as the data length goes to infinity, the estimates still can be asymptotically efficient in the sense of CR lower bound. A numerical example is given to demonstrate the effectiveness and the asymptotic efficiency of the algorithm.
关键词	Asymptotic efficiency FIR system identification quantized input quantized output observations
DOI	10.1007/s11424-017-5305-7
语种	英语
资助项目	National Natural Science Foundation of China[61174042] ; National Natural Science Foundation of China[61403027] ; National Key Research and Development Program of China[2016YFB0901902] ; SKLMCCS[20160105]
WOS研究方向	Mathematics
WOS类目	Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000406359400005
出版者	SPRINGER HEIDELBERG
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/26174
专题	中国科学院数学与系统科学研究院
通讯作者	Guo, Jin
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	He, Yanyu,Guo, Jin. FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs[J]. JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,2017,30(5):1061-1071.
APA	He, Yanyu,&Guo, Jin.(2017).FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs.JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,30(5),1061-1071.
MLA	He, Yanyu,et al."FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs".JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 30.5(2017):1061-1071.