CAJ | 학술논문

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l p (0<p1)范数惩罚约束的自适应算法--加权lp范数惩罚（reweighted lp-norm penalty）LMS算法。该算法的主要思想是在惩罚函数中加入一个更新权值，适当地调节吸引力的大小。计算机仿真实验结果表明了该算法的可取性，并且其在收敛速度和稳态性方面优于现有的稀疏系统辨识方法。
침대경전최소균방（LMS）산법몰유고필충격향응통상구유희소성적특점，일반적희소LMS산법당자괄응추우은태시，대소계수시가과대적흡인력，도치은태오차증대적결점，제출대희소계통진행변식적개진적l p (0<p1)범수징벌약속적자괄응산법--가권lp범수징벌（reweighted lp-norm penalty）LMS산법。해산법적주요사상시재징벌함수중가입일개경신권치，괄당지조절흡인력적대소。계산궤방진실험결과표명료해산법적가취성，병차기재수렴속도화은태성방면우우현유적희소계통변식방법。
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.