为进一步解决最小p阶均方算法（least mean p power, LMP）收敛速度和稳态误差之间的矛盾，提高自适应算法的性能，提出对算法采用不同p值和不同步长进行凸组合（combination of least mean p power, CLMP）的方案.该方案在高斯环境下将独立的大步长LMP₁滤波器和小步长LMP₂滤波器(p₁>p₂)并联，利用分离假设条件进行稳态误差表达式的理论推导.仿真实验证明,在高斯平稳环境下组合滤波器稳态性能较单一滤波器表现得更好，为自适应滤波算法研究提供了一种新思路.

In order to further solve the contradiction between the convergence speed and the steadystate error of the least mean ppower algorithm (LMP) and improve the performance of the adaptive algorithm, a convex combination scheme with different pvalues and variablestep size was proposed（combination of least mean p power, CLMP）to effectively solve this problem. In this scheme, independent large step filter LMP₁ and small step filter LMP₂(p₁>p₂) are connected in parallel in Gaussian environment, and the theoretical derivation of steadystate error expression was carried out by using separation assumption. Simulation results show that the steadystate performance of combined filter is better than that of single filter in Gaussian stationary environment, which provides a new idea for the research of adaptive filtering algorithm.