Reweighted lp-norm constraint least exponentiated square algorithm for sparse system identification

被引:0
|
作者
Luo, Zhengyan [1 ]
Zhao, Haiquan [1 ]
Fang, Zian [1 ]
机构
[1] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Sichuan, Peoples R China
基金
美国国家科学基金会;
关键词
Sparse System; zero-attracting; l(p) norm relaxation; least exponential algorithm; LMS;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
A reweighted l(p)-norm constraint least exponentiated square (RLP-LE2) algorithm is proposed in this paper. The RLP-LE2 algorithm is derived by minimizing a differentiable cost function composed of the exponential error, l(p)-norm of the weight vector, and reweighted zero attractor. We also replace the l(p)-norm of the weighted factor with an approximation to reduce computation complexity. As we know, the algorithms with the zero attractor are designed to identify spare system. So simulations conducted in the system identification are provided to demonstrate the effectiveness of the proposed algorithm. In this paper the proposed algorithm achieves a low steady-state misalignment and robustness under the impulsive environments comparing the performances of this and several exiting algorithms.
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页数:4
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