| 加权[lp]范数LMS算法的稀疏系统辨识 |
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| 引用本文： | 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,2013,49(13):194-197. |
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| 作者姓名： | 刘遵雄 秦 宾 王树成 |
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| 作者单位： | 华东交通大学 信息工程学院，南昌 330013 |
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| 摘 要： | 针对经典最小均方（LMS）算法没有考虑冲击响应通常具有稀疏性的特点，一般的稀疏LMS算法当自适应趋于稳态时，对小系数施加过大的吸引力，导致稳态误差增大的缺点，提出对稀疏系统进行辨识的改进的lp(0
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| 关 键 词： | 最小均方（LMS）算法 稀疏系统 [lp]范数 收敛速度 稳态性 |
Research of sparse system identification with reweighted [lp]-norm penalized Least Mean Square algorithm |
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| LIU Zunxiong,QIN Bin,WANG Shucheng.Research of sparse system identification with reweighted [lp]-norm penalized Least Mean Square algorithm[J].Computer Engineering and Applications,2013,49(13):194-197. |
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| Authors: | LIU Zunxiong QIN Bin WANG Shucheng |
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| Affiliation: | College of Information Engineering, East China Jiaotong University, Nanchang 330013, China |
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| 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. |
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| Keywords: | Least Mean Square（LMS） algorithm sparse system [lp]-norm convergence rate steady-state behaviors |
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