Yayın:
Design and analysis of a novel UWB bandpass filter using 3-D EM simulation-based neural network model with HSA

dc.contributor.authorDemirel, Salih
dc.contributor.authorUyanik, Cafer
dc.date.accessioned2026-06-27T13:46:35Z
dc.date.issued2016
dc.description.abstractThis paper presents a new systematic analysis and design of the ultrawide-band bandpass filter by using the 3-D electromagnetic simulation-based multilayer perceptron neural network (MLP NN) model of unit elements. This MLP NN model is utilized efficiently as a fast and accurate model within a harmony search algorithm (HSA) procedure to determine the resultant optimum microstrip structure geometry. Moreover, the validity and efficiency of the HSA is manifested by comparing it with those of the standard metaheuristics, which are genetic and particle swarm algorithms. The filter that shows the best performance is designed and realized. Measurements taken from the realized filter demonstrate the success of this approximation over the band range of 3.1 GHz to 10.6 GHz, with a flat group delay performance within that range.en
dc.description.sponsorshipTurkish Air Force Academy
dc.description.urihttps://doi.org/10.3906/elk-1310-28
dc.identifier.doi10.3906/elk-1310-28
dc.identifier.eissn1303-6203
dc.identifier.endpage668
dc.identifier.issn1300-0632
dc.identifier.issue2
dc.identifier.startpage656
dc.identifier.urihttps://hdl.handle.net/20.500.14981/54687
dc.identifier.volume24
dc.identifier.wos000369325300023
dc.language.isoeng
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
dc.rightsopenAccess
dc.subjectHarmony search algorithm
dc.subjectultrawide-band bandpass filter
dc.subjectmultilayer perceptron neural network
dc.subjectCIRCUIT ANALYSIS
dc.subjectComputer Science
dc.subjectEngineering
dc.titleDesign and analysis of a novel UWB bandpass filter using 3-D EM simulation-based neural network model with HSA
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

Dosyalar

Koleksiyonlar