Efficient design of high pass FIR filter using quantum-behaved particle swarm optimization with weighted mean best position

Supriya Dhabal, Saptarshi Sengupta
{"title":"Efficient design of high pass FIR filter using quantum-behaved particle swarm optimization with weighted mean best position","authors":"Supriya Dhabal, Saptarshi Sengupta","doi":"10.1109/C3IT.2015.7060145","DOIUrl":null,"url":null,"abstract":"Quantum-behaved particle swarm optimization (QPSO) algorithm theoretically guarantees global convergence and has been implemented on a wide suite of continuous optimization problems. In this paper, the nonlinear multimodal optimization problem of high pass FIR filter design is investigated using the weighted mean best QPSO algorithm (WQPSO). The results are compared with competitive techniques such as QPSO keeping PSO and PM as references. It is seen that WQPSO statistically outperforms QPSO in terms of convergence characteristics and ripple performance of the designed filter.","PeriodicalId":402311,"journal":{"name":"Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/C3IT.2015.7060145","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20

Abstract

Quantum-behaved particle swarm optimization (QPSO) algorithm theoretically guarantees global convergence and has been implemented on a wide suite of continuous optimization problems. In this paper, the nonlinear multimodal optimization problem of high pass FIR filter design is investigated using the weighted mean best QPSO algorithm (WQPSO). The results are compared with competitive techniques such as QPSO keeping PSO and PM as references. It is seen that WQPSO statistically outperforms QPSO in terms of convergence characteristics and ripple performance of the designed 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
基于加权平均最优位置的量子粒子群优化设计高通FIR滤波器
量子粒子群优化(QPSO)算法在理论上保证了算法的全局收敛性,并在一系列广泛的连续优化问题上得到了实现。本文利用加权平均最优QPSO算法研究了高通FIR滤波器设计中的非线性多模态优化问题。并以量子粒子群算法和粒子群算法为参考,与已有的量子粒子群算法进行了比较。从统计上看,WQPSO在收敛特性和所设计滤波器的纹波性能方面优于QPSO。
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
Impact of GaN buffer layer thickness on structural and optical properties of AlGaN/GaN based high electron mobility transistor structure grown on Si(111) substrate by plasma assisted molecular beam epitaxy technique Neural network based gene regulatory network reconstruction Facial landmark detection using FAST Corner Detector of UGC-DDMC Face Database of Tripura tribes A method for developing node probability table using qualitative value of software metrics Computational complexity analysis of PTS technique under graphics processing unit
×
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