Proportionate minimum error entropy algorithm for sparse system identification

DOI10.3390/E17095995zbMath1338.94049OpenAlexW2104433269MaRDI QIDQ296351

Haiquan Zhao, Siyuan Peng, Zongze Wu, Badong Chen, Jose C. Principe

Publication date: 15 June 2016

Published in: Entropy (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/e17095995

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Measures of information, entropy (94A17)

Related Items (4)

Controller design for nonlinear and non-Gaussian multivariable systems based on survival information potential criterion ⋮ Research on dual-channel nonlinear acoustic echo cancellation in \(\alpha\)-stable distributed noise environment ⋮ A general zero attraction proportionate normalized maximum correntropy criterion algorithm for sparse system identification ⋮ A proportionate normalized maximum correntropy criterion algorithm with correntropy induced metric constraint for identifying sparse systems

Cites Work

• Some further results on the minimum error entropy estimation
• On the smoothed minimum error entropy criterion
• Stochastic gradient algorithm under \((h, \phi)\)-entropy criterion
• Minimum-entropy estimation in semi-parametric models
• Acoustic MIMO signal processing
• An Enhanced IAF-PNLMS Adaptive Algorithm for Sparse Impulse Response Identification
• Diffusion Information Theoretic Learning for Distributed Estimation Over Network
• Proportionate adaptive algorithms for network echo cancellation
• Information Theoretic Learning
• Nonlinear system identification in impulsive environments
• Adaptive filters with error nonlinearities: mean-square analysis and optimum design

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