Optimization of IIR high pass filter using craziness based particle swarm optimization technique

S. Saha, Annesha Chaudhuri, D. Mandal, R. Kar, S. Ghoshal
{"title":"Optimization of IIR high pass filter using craziness based particle swarm optimization technique","authors":"S. Saha, Annesha Chaudhuri, D. Mandal, R. Kar, S. Ghoshal","doi":"10.1109/SHUSER.2012.6268873","DOIUrl":null,"url":null,"abstract":"In this paper, a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) is used for the design of 8th order infinite impulse response (IIR) digital filter. The proposed optimization technique is a global heuristic search algorithm and better exploration and exploitation of multidimensional search space can be achieved with closely mimicked swarm behaviour in fundamental PSO equation. Performance of the proposed optimization technique is compared with some well accepted evolutionary algorithms such as PSO and real coded genetic algorithm (RGA). From the simulation study it is established that the CRPSO outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality, i.e., the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole-zero analysis justifies the stability of the designed optimized IIR filter.","PeriodicalId":426671,"journal":{"name":"2012 IEEE Symposium on Humanities, Science and Engineering Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Symposium on Humanities, Science and Engineering Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SHUSER.2012.6268873","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In this paper, a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) is used for the design of 8th order infinite impulse response (IIR) digital filter. The proposed optimization technique is a global heuristic search algorithm and better exploration and exploitation of multidimensional search space can be achieved with closely mimicked swarm behaviour in fundamental PSO equation. Performance of the proposed optimization technique is compared with some well accepted evolutionary algorithms such as PSO and real coded genetic algorithm (RGA). From the simulation study it is established that the CRPSO outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality, i.e., the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole-zero analysis justifies the stability of the designed optimized IIR filter.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于疯狂度的粒子群优化技术优化IIR高通滤波器
本文将粒子群算法的一种变体——基于疯狂度的粒子群算法(CRPSO)应用于八阶无限脉冲响应数字滤波器的设计。所提出的优化技术是一种全局启发式搜索算法,通过近似模拟基本粒子群优化方程中的群体行为,可以更好地探索和利用多维搜索空间。将所提优化技术的性能与一些公认的进化算法(如粒子群优化算法和实编码遗传算法)进行了比较。仿真研究表明,CRPSO不仅在设计滤波器的精度上优于RGA和PSO,而且在收敛速度和解的质量上也优于RGA和PSO,即阻带衰减、过渡宽度、通带和阻带波纹。此外,极点-零点分析验证了优化后的IIR滤波器的稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Islamic inheritance claim processes — Non-normality data traits and best estimator choice Treatment effectiveness of continuous passive motion machine during post-operative treatment of anterior cruciate ligament patients Harmonic elimination in switching table-based direct torque control of five-phase PMSM using matrix converter Digital stable IIR high pass filter optimization using PSO-CFIWA IPv6 attack scenarios testbed
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1