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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Autores principales:	Sihai Guan, Qing Cheng, Yong Zhao, Bharat Biswal
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cf18ddced64944ae991980004f63de00
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spelling	oai:doaj.org-article:cf18ddced64944ae991980004f63de002021-12-02T20:17:05ZRobust adaptive filtering algorithms based on (inverse)hyperbolic sine function.1932-620310.1371/journal.pone.0258155https://doaj.org/article/cf18ddced64944ae991980004f63de002021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258155https://doaj.org/toc/1932-6203Recently, 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.Sihai GuanQing ChengYong ZhaoBharat BiswalPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258155 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sihai Guan Qing Cheng Yong Zhao Bharat Biswal Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.
description	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.
format	article
author	Sihai Guan Qing Cheng Yong Zhao Bharat Biswal
author_facet	Sihai Guan Qing Cheng Yong Zhao Bharat Biswal
author_sort	Sihai Guan
title	Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.
title_short	Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.
title_full	Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.
title_fullStr	Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.
title_full_unstemmed	Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.
title_sort	robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/cf18ddced64944ae991980004f63de00
work_keys_str_mv	AT sihaiguan robustadaptivefilteringalgorithmsbasedoninversehyperbolicsinefunction AT qingcheng robustadaptivefilteringalgorithmsbasedoninversehyperbolicsinefunction AT yongzhao robustadaptivefilteringalgorithmsbasedoninversehyperbolicsinefunction AT bharatbiswal robustadaptivefilteringalgorithmsbasedoninversehyperbolicsinefunction
_version_	1718374431593594880

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

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