改进的凸组合最小均方算法

Improved Adaptive Convex Combination of Least Mean Square Algorithm

凸组合最小均方(CLMS)算法能够克服传统最小均方算法收敛速率、跟踪性能和稳态误差之间的矛盾.但传统CLMS算法使用最速下降法推导参数导致其搜索路径呈"之"字形而使收敛速率变慢,为了解决这个问题,采用共轭梯度法实现参数的更新,同时使用双曲正切函数拟合Sigmoid函数来降低算法的运算复杂度.为进一步提高算法性能,在所设计的基础上附加瞬时转移结构实现优化.仿真结果证明,改进算法与传统CLMS、变步长CLMS相比,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.

The convex combination of least mean square（ CLMS） algorithm can overcome the contradiction between convergence rate,tracking performance and steady state error of traditional least mean square algorithm. However,in the normal adaptive CLMS algorithm,the rule for modifying mixing parameter is based on the steepest descent method. When the algorithm converges,it will generate zigzag phenomena,which can make the convergence speed become slowly. In order to solve this problem,a new rule based on the conjugate gradient method is proposed in this paper. At the same time,modified hyperbolic tangent function is used to reduce computational complexity. Meanwhile,instantaneous transfer scheme is used to further optimize the performance. Theoretical analysis and simulation results demonstrate that under different simulation environment,the proposed algorithm performs good property of mean square and tracking compared with the traditional CLMS and variable step-size CLMS algorithms.