基于粒子群隐式空间映射算法设计的双频滤波器  

Particle swarm implicit space mapping algorithm design of dual-band filter

作　　者：张友俊[1] 王连栋[1] 

机构地区：[1]上海海事大学信息工程学院,上海201306

出　　处：《电子元件与材料》2016年第12期57-60,共4页Electronic Components And Materials

基　　金：国家自然科学基金资助项目(No.61131002)

摘　　要：将粒子群算法(Particle Swarm Optimization,PSO)用在隐式空间映射(Implicit Space Mapping,ISM)的参数提取中,可以有效改善参数提取过程中算法的不收敛性。首次在参数提取中引入PSO,主要的研究内容是改进了ISM算法。通过改进ISM算法中粗糙模型(Coarse Model,CM)与精细模型(Fine Model,FM)之间的参数映射,可以明显减少迭代次数。以一个双模滤波器为例,利用粒子群ISM算法设计了一个可以工作在无线局域网(WLAN)频段的微带双频带滤波器,中心频率分别是2.45 GHz和5.25 GHz。滤波器经过3次迭代并进行微调后达到了设计指标。由此可见,引入PSO之后显著地减少了在FM中的仿真次数,有效地提高了滤波器的设计效率。The particle swarm optimization（PSO） algorithm used in implicit space mapping（ISM） parameter extraction can improve the dis-convergence of the parameter extraction algorithm. This paper first introduced PSO in parameter extraction, and the main aim of the research was to improve the ISM algorithm. It is found that improving the parameter mapping in the ISM algorithm between the coarse model（CM） and the fine model（FM） significantly reduces the number of iterations. Take the dual-mode filter as a for example, by using the PSO algorithm of ISM a micro-strip dual-frequency band filter worked in the wireless local area network（WLAN） spectrum was designed, whose center frequencies were 2.45 GHz and 5.25 GHz, respectively. After three iterations and fine-tuning, the filter reached the design target. Thus, the introduction of PSO significantly reduces the number of simulations in the FM, and effectively improves the design efficiency of the filter.

关 键 词：粒子群算法 隐式空间映射算法 参数提取 粗糙模型 精细模型 双频带滤波器 

分 类 号：TN713[电子电信—电路与系统] TP391[自动化与计算机技术—计算机应用技术]