FIR滤波器设计:基于PSO优化算法的频率采样法

Design of FIR Filter:Based on the Frequency Sampling Technique of Particle Swarm Optimization(PSO)

粒子群优化算法(PSO)是一种具有智能优化进化计算技术。该算法基于对鸟群活动行为观察,利用群体中的个体对信息的共享这一特点,使整个群体活动的问题求解空间中产生的一种从无序到有序的演化过程,从而获得最优解。文中介绍了PSO在频率采样技术中的应用。结合PSO算法,将滤波器过渡带样本值作为优化变量,通过PSO算法确定的频率过渡带样本值是最优的。

Particle swarm optimization（PSO） is a kind of intelligent optimization and evolutionary computation technique. The algorithm is based on the observation of bird flock＇s activities, With the characteristic of information sharing between individuals and groups, promoting the solution space of the entire group activities to produce a process from disordered state to ordered. From which we can achieve the optimum solution. This paper introduces the application of PSO into frequency sampling technique. Combined with the PSO algorithm, and setting the filter ‘s value of transition bands sample as optimal variables, then we can draw a conclusion that the value of transition bands sample determined by the PSO algorithm is optimum.