Reweighted l1 regularized TLS linear neuron for the sparse system identification

This paper presents a neural approach for the parameter estimation of adaptive sparse system identification based on a MCA EXIN (minor component analysis by excitatory and inhibitory learning) linear neuron for TLS (total least squares) problem. We incorporate a log-sum (or Reweighted l1) penalty in...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2016-01, Vol.173, p.1972-1975
Main Authors: Lim, JunSeok, Pang, Heesuk
Format: Article
Language:English
Subjects:
Reweighted l1 norm
Sparse system identification
TLS EXIN
Total least squares
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Summary:This paper presents a neural approach for the parameter estimation of adaptive sparse system identification based on a MCA EXIN (minor component analysis by excitatory and inhibitory learning) linear neuron for TLS (total least squares) problem. We incorporate a log-sum (or Reweighted l1) penalty into the cost function of TLS EXIN (total least squares by excitatory and inhibitory learning) in order to identify the sparse system. The given computer simulations illustrate that the neural approach considerably outperforms the existing TLS EXIN method as well as the LMS-type adaptive methods in the sparse system.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2015.08.020