摘要
给出一种基于粒子群优化算法(PSO)的模拟滤波器优化设计方法。传统的模拟滤波器的精度与效率均较差,引入PSO算法可对滤波器参数进行寻优。将滤波器的设计问题转化为滤波器参数的优化问题,然后利用粒子群优化算法对整个参数空间进行高效搜索以获得最优解;通过变异、重新随机化及采用自适应的惯性权重,提高了算法的搜索效率及收敛性。实例计算表明了算法在该类问题中的有效性和可行性。
An optimization scheme of analog filter design based on the particle swarm optimization （ PSO） algorithm are proposed. Traditional analog filter is imprecise and inefficient. The optimal parameters of the filters can be obtained by introducing the PSO algorithm. By translating the design of the filter into the optimization of its parameters, PSO can be used to explore the whole parameters space effectively in parallel in order to achieve the optimum solution. With adopting mutation and re-randomizing operator and introducing adaptive inertia weight, the global convergence performance and the effectiveness of the proposed algorithm is enhanced. The practical example shows that the algorithm is effective and feasible.
出处
《自动化仪表》
CAS
2007年第11期8-11,共4页
Process Automation Instrumentation
关键词
粒子群优化
群体智能
进化算法
滤波器优化
模拟滤波器
Particle swarm optimization（ PSO） Swarm intelligence Evolutionary algorithm Filter optimization Analog filter
