{"title":"Design of FIR band-pass digital filter using Heuristic Optimization Technique: A comparison","authors":"Haroon Sidhu, Raminderjit Kaur, Balraj Singh","doi":"10.1109/CCINTELS.2015.7437916","DOIUrl":null,"url":null,"abstract":"The design of Digital Finite Impulse Response (FIR) digital band-pass filter using two Heuristic Optimization Technique have been implemented. Digital FIR Filters are better than Infinite Impulse Response Filters due to their stability and having linear phase. This paper explores the two heuristic optimization techniques namely Particle Swarm Optimization and Differential Evolution. The evaluation of performance of DE algorithm and PSO algorithm has been done and results performs have been compared on the basis of their control parameters. The achieved results show that the Differential Evolution Algorithm better than that of Particle Swarm Optimization in terms of achieved magnitude error and ripples in pass-band and stop-band.","PeriodicalId":131816,"journal":{"name":"2015 Communication, Control and Intelligent Systems (CCIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Communication, Control and Intelligent Systems (CCIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCINTELS.2015.7437916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The design of Digital Finite Impulse Response (FIR) digital band-pass filter using two Heuristic Optimization Technique have been implemented. Digital FIR Filters are better than Infinite Impulse Response Filters due to their stability and having linear phase. This paper explores the two heuristic optimization techniques namely Particle Swarm Optimization and Differential Evolution. The evaluation of performance of DE algorithm and PSO algorithm has been done and results performs have been compared on the basis of their control parameters. The achieved results show that the Differential Evolution Algorithm better than that of Particle Swarm Optimization in terms of achieved magnitude error and ripples in pass-band and stop-band.
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