Variable Kernel Width Algorithm of Generalized Maximum Correntropy Criteria for Censored Regression

The constant kernel width of generalized maximum correntropy criteria (GMCC) has arisen that the steady-state error and convergence speed can be mutually exclusive. To solve this problem, this brief proposes the variable kernel width (VKW) GMCC algorithm. Actually, due to the censored problem, the o...

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Veröffentlicht in:IEEE transactions on circuits and systems. II, Express briefs Jg. 69; H. 3; S. 1877 - 1881
Hauptverfasser: Zhao, Haiquan, Chen, Bing, Zhu, Yingying, He, Xiaoqiong, Shu, Zeliang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
adaptive filtering
Adaptive filters
Algorithms
censored regression
Convergence
Criteria
Filtering algorithms
Generalized maximum correntropy criteria
Kernel
Kernels
Optimized production technology
Random noise
Signal processing algorithms
Steady-state
variable kernel width
ISSN:1549-7747, 1558-3791
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Zusammenfassung:The constant kernel width of generalized maximum correntropy criteria (GMCC) has arisen that the steady-state error and convergence speed can be mutually exclusive. To solve this problem, this brief proposes the variable kernel width (VKW) GMCC algorithm. Actually, due to the censored problem, the output data value beyond the limit of the recording device can not be well observed. In this case, we further developed a variable kernel width GMCC algorithm based on censored regression (CR-VKWGMCC). Simulation results show that the proposed CR-VKWGMCC algorithm has excellent performance in both Gaussian and non Gaussian noise.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2021.3103504