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在对基本LMS算法分析的基础上,通过构造步长因子μ与误差信号e(n)之间的非线性函数,提出一种新的变步长最小均方误差(LMS)算法,并且分析了参数的取值对算法性能的影响。该算法通过调整步长参数,使权向量达到最优,有效改善了收敛速度与稳态误差的性能。理论分析和仿真结果表明,与基本LMS算法以及部分同类变步长LMS算法相比,该算法具有更快的收敛速度和更小的稳态误差,进一步验证了新算法优于这里所述其他算法。
Based on the analysis of the basic LMS algorithm, a new variable step size minimum mean square error (LMS) algorithm is proposed by constructing the nonlinear function between the step size factor μ and the error signal e (n) The influence of the parameter value on the performance of the algorithm. The algorithm adjusts the step parameters to optimize the weight vector, which effectively improves the performance of convergence speed and steady-state error. The theoretical analysis and simulation results show that the proposed algorithm has faster convergence speed and smaller steady-state error than the basic LMS algorithm and some similar variable-length LMS algorithms, and further verifies that the new algorithm outperforms the other algorithms described here .