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Threshold Selection and Resource Allocation for Quantized Identification

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This paper is concerned with the optimal threshold selection and resource allocation prob-lems of quantized identification,whose aims are improving identification efficiency under limited re-sources.Firstly,the first-order asymptotically optimal quantized identification theory is extended to the weak persistent excitation condition.Secondly,the characteristics of time and space complexities are established based on the Cramér-Rao lower bound of quantized systems.On these basis,the op-timal selection methods of fixed thresholds and adaptive thresholds are established under aperiodic signals,which answer how to achieve the best efficiency of quantized identification under the same time and space complexity.In addition,based on the principle of maximizing the identification efficiency under a given resource,the optimal resource allocation methods of quantized identification are given for the cases of fixed thresholds and adaptive thresholds,respectively,which show how to balance time and space complexity to realize the best identification efficiency of quantized identification.

Quantized outputresource allocationsystem identificationthreshold selection

WANG Ying、LI Xin、ZHAO Yanlong、ZHANG Ji-Feng

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Key Laboratory of Systems and Control,Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China

School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China

国家重点研发计划国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金CAS Project for Young Scientists in Basic Research,ChinaPostdoctoral Science FoundationGuozhi Xu Postdoctoral Research Foundation

2018YFA0703800T22937706202530662303452122263051YSBR-0082022M720159

2024

10.1007/s11424-024-3369-8

系统科学与复杂性学报(英文版)
中国科学院系统科学研究所

系统科学与复杂性学报(英文版)

EI
影响因子：0.181
ISSN：1009-6124
年,卷(期)：2024.37(1)
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