针对现有变步长凸组合LMS(VSCLMS)算法需预先决定行为参数的问题，提出了一种新的VSCLMS算法. 该算法对变步长滤波器按步进行分析，以最大均方权值偏差准则为步长选择依据，对固定步长滤波器则采用稳态最小均方误差准则为步长选择依据. 理论分析与仿真实验表明，该算法能在噪声、时变、甚至非平稳的环境下保持良好的随动性能，并在收敛的各个阶段均保持快速而稳定的均方特性. 与现有VSCLMS及CLMS算法相比，具有更快的收敛速度，且稳态均方性能与跟踪性能均更好.

The existing variable step-size convex combination of LMS (VSCLMS) algorithm needs to predetermine the behavioral parameters. To avoid this, the paper proposes a new variable step-size adaptive filter with analytical minimization of the ensemble-averaged mean-square weight error. Instead of variable step-size parameters in the original VSCLMS, the proposed algorithm uses a constant step-size parameter derived based on steady-state MSWE minimization. Theoretical analysis and simulations show that the proposed algorithm has a better tracking performance in the presence of noise and in a time-varying and even non-stable environment. Besides, it converges fast and is stable in the convergence process, and is better in these aspects as compared with the original VSCLMS and CLMS algorithms.