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Correntropy-Induced Metric-Based Variable Step Size Normalized Least Mean Square Algorithm in Sparse System Identification

  • Rajni YadavEmail author
  • Sonia Jain
  • C. S. Rai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)

Abstract

This paper incorporates a correntropy-induced metric (CIM)-based sparsity constraint into variable step size normalized least mean square (VSSNLMS) algorithm for identification of sparse system corrupted by additive noise. The proposed CIM-VSSNLMS algorithm makes a good trade-off between convergence characteristics, filter stability, and steady state error. The proposed (CIM-VSSNLMS) algorithm incorporates a variable step size that accelerates the convergence characteristics and lowers the normalized misalignment (NMSA) error with respect to CIM-NLMS with constant value of step size. An expression for variant step size is derived under the stability condition of proposed algorithm. Finally, the implementation of the proposed algorithm is carried out in MATLAB software to manifest the improved estimated behavior in the identification of sparse system.

Keywords

Gaussian input Sparse system Stability Correntropy Convergence rate Normalized misalignment 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Maharaja Agrasen Institute of TechnologyGuru Gobind Singh Indraprastha UniversityNew DelhiIndia
  2. 2.University School of Information, Communication and TechnologyGuru Gobind Singh Indraprastha UniversityNew DelhiIndia

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