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FIR systems identification under quantized output observations and a large class of persistently exciting quantized inputs
He, Yanyu1; Guo, Jin2
2017-10-01
Source PublicationJOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
ISSN1009-6124
Volume30Issue:5Pages:1061-1071
AbstractThis 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.
KeywordAsymptotic efficiency FIR system identification quantized input quantized output observations
DOI10.1007/s11424-017-5305-7
Language英语
Funding ProjectNational 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 Research AreaMathematics
WOS SubjectMathematics, Interdisciplinary Applications
WOS IDWOS:000406359400005
PublisherSPRINGER HEIDELBERG
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/26174
Collection中国科学院数学与系统科学研究院
Corresponding AuthorGuo, Jin
Affiliation1.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
Recommended Citation
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.
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