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Threshold Selection and Resource Allocation for Quantized Identification

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Abstract

This paper is concerned with the optimal threshold selection and resource allocation problems of quantized identification, whose aims are improving identification efficiency under limited resources. 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 Cramer-Rao lower bound of quantized systems. On these basis, the optimal 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.

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Authors and Affiliations

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

    Ying Wang, Xin Li, Yanlong Zhao & Ji-Feng Zhang

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

    Ying Wang, Xin Li, Yanlong Zhao & Ji-Feng Zhang

Authors
  1. Ying Wang
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  2. Xin Li
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  3. Yanlong Zhao
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  4. Ji-Feng Zhang
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Corresponding author

Correspondence to Yanlong Zhao.

Ethics declarations

ZHAO Yanlong is an editorial board member for Journal of Systems Science & Complexity and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Additional information

This research was supported by the National Key R&D Program of China under Grant No. 2018YFA0703800, the National Natural Science Foundation of China under Grant Nos. T2293770, 62025306, 62303452, and 122263051, CAS Project for Young Scientists in Basic Research under Grant No. YSBR-008, China Postdoctoral Science Foundation under Grant No. 2022M720159, and Guozhi Xu Postdoctoral Research Foundation.

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Cite this article

Wang, Y., Li, X., Zhao, Y. et al. Threshold Selection and Resource Allocation for Quantized Identification. J Syst Sci Complex 37, 204–229 (2024). https://doi.org/10.1007/s11424-024-3369-8

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  • DOI: https://doi.org/10.1007/s11424-024-3369-8

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