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{"title":"基于遗传算法改进神经网络的纳米纤维静电纺丝控制参数同步效应定量研究","authors":"Shayan Seyedin, S. Maghsoodloo, V. Mottaghitalab","doi":"10.4018/IJCCE.2017010102","DOIUrl":null,"url":null,"abstract":"Inthisarticle,modifiedneuralnetworksusinggeneticalgorithmswereemployedtoinvestigatethe simultaneouseffectsoffourofthemostimportantparameters,namely;solutionconcentration(C); spinningdistance(d);appliedvoltage(V);andvolumeflowrate(Q)onmeanfiberdiameter(MFD), as well as standard deviation of fiber diameter (StdFD) in electrospinning of polyvinyl alcohol (PVA)nanofibers.Geneticalgorithmoptimizedneuralnetworks(GANN)wereusedformodeling theelectrospinningprocess.Theresultsindicatebetterexperimentalconditionsandmorepredictive abilityofGANNs.Therefore,theapproachofusinggeneticalgorithmstooptimizeneuralnetworksfor modelingtheelectrospinningprocesshasbeensuccessful.RSMcouldbeemployedwhenstatistical analysis,quantitativestudyoftheeffectsoftheparametersandvisualizationoftheresponsesurfaces areofinterest,whereasinthecaseofmodelingtheprocessandpredictingnewconditions,GANN isamorepowerfultoolandpresentsmoredesirableresults. KEywoRdS Electrospinning, Empirical Modeling, Genetic Algorithm Optimized Neural Networks (GANN), Response Surface Methodology","PeriodicalId":132974,"journal":{"name":"Int. J. Chemoinformatics Chem. Eng.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Quantitative Study on Simultaneous Effects of Governing Parameters in Electrospinning of Nanofibers using Modified Neural Network using Genetic Algorithm\",\"authors\":\"Shayan Seyedin, S. Maghsoodloo, V. Mottaghitalab\",\"doi\":\"10.4018/IJCCE.2017010102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Inthisarticle,modifiedneuralnetworksusinggeneticalgorithmswereemployedtoinvestigatethe simultaneouseffectsoffourofthemostimportantparameters,namely;solutionconcentration(C); spinningdistance(d);appliedvoltage(V);andvolumeflowrate(Q)onmeanfiberdiameter(MFD), as well as standard deviation of fiber diameter (StdFD) in electrospinning of polyvinyl alcohol (PVA)nanofibers.Geneticalgorithmoptimizedneuralnetworks(GANN)wereusedformodeling theelectrospinningprocess.Theresultsindicatebetterexperimentalconditionsandmorepredictive abilityofGANNs.Therefore,theapproachofusinggeneticalgorithmstooptimizeneuralnetworksfor modelingtheelectrospinningprocesshasbeensuccessful.RSMcouldbeemployedwhenstatistical analysis,quantitativestudyoftheeffectsoftheparametersandvisualizationoftheresponsesurfaces areofinterest,whereasinthecaseofmodelingtheprocessandpredictingnewconditions,GANN isamorepowerfultoolandpresentsmoredesirableresults. KEywoRdS Electrospinning, Empirical Modeling, Genetic Algorithm Optimized Neural Networks (GANN), Response Surface Methodology\",\"PeriodicalId\":132974,\"journal\":{\"name\":\"Int. J. Chemoinformatics Chem. Eng.\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Chemoinformatics Chem. Eng.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJCCE.2017010102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Chemoinformatics Chem. Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJCCE.2017010102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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A Quantitative Study on Simultaneous Effects of Governing Parameters in Electrospinning of Nanofibers using Modified Neural Network using Genetic Algorithm
Inthisarticle,modifiedneuralnetworksusinggeneticalgorithmswereemployedtoinvestigatethe simultaneouseffectsoffourofthemostimportantparameters,namely;solutionconcentration(C); spinningdistance(d);appliedvoltage(V);andvolumeflowrate(Q)onmeanfiberdiameter(MFD), as well as standard deviation of fiber diameter (StdFD) in electrospinning of polyvinyl alcohol (PVA)nanofibers.Geneticalgorithmoptimizedneuralnetworks(GANN)wereusedformodeling theelectrospinningprocess.Theresultsindicatebetterexperimentalconditionsandmorepredictive abilityofGANNs.Therefore,theapproachofusinggeneticalgorithmstooptimizeneuralnetworksfor modelingtheelectrospinningprocesshasbeensuccessful.RSMcouldbeemployedwhenstatistical analysis,quantitativestudyoftheeffectsoftheparametersandvisualizationoftheresponsesurfaces areofinterest,whereasinthecaseofmodelingtheprocessandpredictingnewconditions,GANN isamorepowerfultoolandpresentsmoredesirableresults. KEywoRdS Electrospinning, Empirical Modeling, Genetic Algorithm Optimized Neural Networks (GANN), Response Surface Methodology
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