中文摘要：

为了避免单个滤波器在收敛速率与稳态误差上的相互制约,从而导致系统性能下降的问题,文章采用凸组合最小均方算法,将快速滤波器与慢速滤波器并联使用来解决。为进一步改善性能,提出了一种采用瞬时转移结构的低复杂度凸组合最小均方算法。在该算法中,分别使用修正反正切函数和sign函数对参数λ（n）和a n（）的更新迭代公式做了简化和改进,同时为加快算法的收敛速率引入一个长度为N0的窗实现瞬时权值转移。仿真结果表明：文中改进算法在噪声、相关信号输入以及非平稳环境下能够保持较好的均方性能和跟踪性能,并且具备更快的收敛速率。

英文摘要：

In order to solve the problem that a single LMS filter is restricted by mutual influence at convergence speed and stable state error to lead to the performance decrease of the recognition system, the convex combination of least-mean-square algorithm is employed in this paper by paralleling use of a fast and a slow LMS filter. To further improve the algorithm＇s capability, a new low-complexity convex combination of CLMS algorithm is proposed by improving the traditional CLMS. The proposed algorithm simplifies and improves the renewal iterative formula of parameters＇ sum by using modified arc tangent function and sign function respectively. Meanwhile the paper employs an instantaneous transfer scheme combined with the window length of to accelerate the convergence rate. Theoretical analysis and simulation results suggest that under the conditions of the influence of noise, relative signal input and unstable environment, the proposed algorithm can not only maintain a superior capability o~ tracking and mean square, but also possess a higher convergence rate.