Reweighted l₁ regularized TLS linear neuron for the sparse system identification

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• 作者：JunSeok Lim ; ^{jslim@sejong.ac.kr" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Heesuk PangAuthor Vitae
• 关键词：Total least squares ; Sparse system identification ; Reweighted l1 norm ; TLS EXIN
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P3
• 页码：1972-1975
• 全文大小：581 K

文摘

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.