A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems
A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive...
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doaj-073016d19e894a80997bff6f90cf109d2020-11-24T22:52:12ZengMDPI AGSymmetry2073-89942018-12-01101268310.3390/sym10120683sym10120683A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse SystemsYingsong Li0Yanyan Wang1Laijun Sun2College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaKey Laboratory of Electronics Engineering, College of Heilongjiang Province, Heilongjiang University, Harbin 150080, ChinaA proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive filter, maximum correntropy criterion (MCC) algorithm, and zero attraction theory. The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. The derivation of the CIM-PNMCC is given. The proposed algorithms are used for evaluating the sparse systems in a non-Gaussian environment and the simulation results show that the expanded normalized maximum correntropy criterion (NMCC) adaptive filter algorithms achieve better performance than those of the squared proportionate algorithms such as proportionate normalized least mean square (PNLMS) algorithm. The proposed algorithm can be used for estimating finite impulse response (FIR) systems with symmetric impulse response to prevent the phase distortion in communication system.https://www.mdpi.com/2073-8994/10/12/683sparse adaptive filteringnormalized maximum correntropy criterionPNLMS algorithmzero attraction algorithmnon-Gaussian noise |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yingsong Li Yanyan Wang Laijun Sun |
spellingShingle |
Yingsong Li Yanyan Wang Laijun Sun A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems Symmetry sparse adaptive filtering normalized maximum correntropy criterion PNLMS algorithm zero attraction algorithm non-Gaussian noise |
author_facet |
Yingsong Li Yanyan Wang Laijun Sun |
author_sort |
Yingsong Li |
title |
A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems |
title_short |
A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems |
title_full |
A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems |
title_fullStr |
A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems |
title_full_unstemmed |
A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems |
title_sort |
proportionate normalized maximum correntropy criterion algorithm with correntropy induced metric constraint for identifying sparse systems |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2018-12-01 |
description |
A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive filter, maximum correntropy criterion (MCC) algorithm, and zero attraction theory. The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. The derivation of the CIM-PNMCC is given. The proposed algorithms are used for evaluating the sparse systems in a non-Gaussian environment and the simulation results show that the expanded normalized maximum correntropy criterion (NMCC) adaptive filter algorithms achieve better performance than those of the squared proportionate algorithms such as proportionate normalized least mean square (PNLMS) algorithm. The proposed algorithm can be used for estimating finite impulse response (FIR) systems with symmetric impulse response to prevent the phase distortion in communication system. |
topic |
sparse adaptive filtering normalized maximum correntropy criterion PNLMS algorithm zero attraction algorithm non-Gaussian noise |
url |
https://www.mdpi.com/2073-8994/10/12/683 |
work_keys_str_mv |
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