Correntropy induced metric penalized sparse RLS algorithm to improve adaptive system identification

Guan Gui, Linglong Dai, Baoyu Zheng, Li Xu, Fumiyuki Adachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l-1-norm). However, existing sparse RLS algorithms do not exploit the sparsity efficiently. In this paper, an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically, we adopt a well-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).

Original languageEnglish
Title of host publication2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509016983
Publication statusPublished - 2016 Jul 5
Event83rd IEEE Vehicular Technology Conference, VTC Spring 2016 - Nanjing, China
Duration: 2016 May 152016 May 18

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Other83rd IEEE Vehicular Technology Conference, VTC Spring 2016


  • Adaptive system identification.
  • Correntropy induced metric (CIM)
  • Recursive least square (RLS)
  • Sparse adaptive filtering

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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