计算机工程与应用Issue(13)：194-197,4.DOI:10.3778/j.issn.1002-8331.1111-0148
加权lp范数LMS算法的稀疏系统辨识
Research of sparse system identification with reweighted lp-norm penalized Least Mean Square algorithm
摘要
Abstract
Because the standard Least Mean Square(LMS)algorithm does not consider the sparsity of the impulse response and the general sparse LMS algorithm gives much large attraction to the small factor, leading to increased steady-state error, a new approach for sparse system identification is proposed. This new adaptive algorithm is named reweighted lp -norm penalized LMS algorithm. The main idea of this algorithm is to add an updated weight in the penalty function for appropriately adjusting attraction. The simulation results confirm the correctness of the theory, and the proposed algorithm in both convergence rate and steady-state behaviors is better than the existing sparse system identification methods.关键词
最小均方(LMS)算法/稀疏系统/lp范数/收敛速度/稳态性Key words
Least Mean Square(LMS)algorithm/sparse system/lp-norm/convergence rate/steady-state behaviors分类
信息技术与安全科学引用本文复制引用
刘遵雄,秦宾,王树成..加权lp范数LMS算法的稀疏系统辨识[J].计算机工程与应用,2013,(13):194-197,4.基金项目
国家自然科学基金（No.61065003）；国家教育部人文社会科学研究规划基金（No.09YJA630036）；江西省自然科学基金（No.2010GZS0034）。 （）
