A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm

A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm

A novel adaptive filtering (AF) algorithm is proposed for group-sparse system identifications. In the devised algorithm, a novel mixed error criterion (MEC) with two-order logarithm error, <i>p</i>-order errors and group sparse constraint method is devised to give a resistant to the impu...

Full description

Bibliographic Details
Main Authors: Yingsong Li, Aleksey Cherednichenko, Zhengxiong Jiang, Wanlu Shi, Jinqiu Wu
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Symmetry
Subjects:
system identification
group sparse constraint
mixed error criterion
impulsive noise environments
Online Access:https://www.mdpi.com/2073-8994/11/5/697
id doaj-cd05314ddc514860bc886e7de9aa188b
record_format Article
spelling doaj-cd05314ddc514860bc886e7de9aa188b2020-11-25T01:36:54ZengMDPI AGSymmetry2073-89942019-05-0111569710.3390/sym11050697sym11050697A Novel Generalized Group-Sparse Mixture Adaptive Filtering AlgorithmYingsong Li0Aleksey Cherednichenko1Zhengxiong Jiang2Wanlu Shi3Jinqiu Wu4College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaBeijing Institute of Control and Electronic Technology, Beijing 10038, ChinaA novel adaptive filtering (AF) algorithm is proposed for group-sparse system identifications. In the devised algorithm, a novel mixed error criterion (MEC) with two-order logarithm error, <i>p</i>-order errors and group sparse constraint method is devised to give a resistant to the impulsive noise. The proposed group-sparse MEC can fully use the known group-sparse characteristics in the cluster sparse systems, and it is derived and analyzed in detail. Various simulations are presented and analyzed to give a verification on the effectiveness of the developed group-sparse MEC algorithms, and the simulated results shown that the developed algorithm outperforms the previously developed sparse AF algorithms for identifying the systems.https://www.mdpi.com/2073-8994/11/5/697system identificationgroup sparse constraintmixed error criterionimpulsive noise environments
collection DOAJ
language English
format Article
sources DOAJ
author Yingsong Li
Aleksey Cherednichenko
Zhengxiong Jiang
Wanlu Shi
Jinqiu Wu
spellingShingle Yingsong Li
Aleksey Cherednichenko
Zhengxiong Jiang
Wanlu Shi
Jinqiu Wu
A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
Symmetry
system identification
group sparse constraint
mixed error criterion
impulsive noise environments
author_facet Yingsong Li
Aleksey Cherednichenko
Zhengxiong Jiang
Wanlu Shi
Jinqiu Wu
author_sort Yingsong Li
title A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
title_short A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
title_full A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
title_fullStr A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
title_full_unstemmed A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
title_sort novel generalized group-sparse mixture adaptive filtering algorithm
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2019-05-01
description A novel adaptive filtering (AF) algorithm is proposed for group-sparse system identifications. In the devised algorithm, a novel mixed error criterion (MEC) with two-order logarithm error, <i>p</i>-order errors and group sparse constraint method is devised to give a resistant to the impulsive noise. The proposed group-sparse MEC can fully use the known group-sparse characteristics in the cluster sparse systems, and it is derived and analyzed in detail. Various simulations are presented and analyzed to give a verification on the effectiveness of the developed group-sparse MEC algorithms, and the simulated results shown that the developed algorithm outperforms the previously developed sparse AF algorithms for identifying the systems.
topic system identification
group sparse constraint
mixed error criterion
impulsive noise environments
url https://www.mdpi.com/2073-8994/11/5/697
work_keys_str_mv AT yingsongli anovelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT alekseycherednichenko anovelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT zhengxiongjiang anovelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT wanlushi anovelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT jinqiuwu anovelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT yingsongli novelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT alekseycherednichenko novelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT zhengxiongjiang novelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT wanlushi novelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
AT jinqiuwu novelgeneralizedgroupsparsemixtureadaptivefilteringalgorithm
_version_ 1725061021964435456

Similar Items