Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More...

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Bibliographic Details
Published in:PloS one 2021-10, Vol.16 (10), p.e0258155-e0258155
Main Authors: Guan, Sihai, Cheng, Qing, Zhao, Yong, Biswal, Bharat
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Adaptive filters
Algorithms
Analysis
Biology and Life Sciences
Biomedical engineering
Computer applications
Engineering
Engineering and Technology
Estimation accuracy
Hyperbolic functions
Information management
Interference
Laboratories
Noise
Normal distribution
Physical Sciences
Random noise
Research and Analysis Methods
Robustness
System identification
Trigonometric functions
Trigonometrical functions
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Summary:Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based on HSF, and extend the HSF algorithm to another novel adaptive filtering algorithm based on IHSF; then continue to analyze the computational complexity for HSF and IHSF; finally, validation of the analyses and superiority of the proposed algorithm via simulations. The HSF and IHSF algorithms can attain superior steady-state performance and stronger robustness in impulsive interference than several existing algorithms for different system identification scenarios, under Gaussian noise and impulsive interference, demonstrate the superior performance achieved by HSF and IHSF over existing adaptive filtering algorithms with different hyperbolic functions.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0258155