基于粒子群算法的自适应陷波器设计

Design of Adaptive Notch Filter Based on Particle Swarm Optimization Algorithm

传统的基于LMS算法的自适应陷波器,由于极易受到步长及其它参数的影响,学习曲线并不理想。文章在分析步长对基于LMS算法自适应陷波器的影响的基础上,将粒子群(PSO)算法应用到自适应陷波器的设计中。通过仿真结果显示,基于PSO算法的自适应陷波器收敛速度快、具有鲁棒性,优于传统的基于LMS的自适应陷波器,从而证明其有效性、可行性及工程价值。

Due to the traditional adaptive notch filters based on LMS algorithm are vulnerable to step length and other parameters, the learning curve is not ideal. The effect of step length on adaptive notch filters based on LMS algorithm was investigated initially, then particle swarm optimization （PSO） algorithm was applied to the design of adaptive notch filter. Simulation results showed that adaptive notch filters based on PSO algorithm had advantage of fast convergence and a trait of robustness, which superior to the traditional adaptive notch filter based on LMS. So its effectiveness, feasibility and engineering value were proved.