RBF神经网络与遗传算法的离心泵优化设计-豆柴文库

您所在位置：网站首页 / RBF神经网络与遗传算法的离心泵优化设计.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

RBF神经网络与遗传算法的离心泵优化设计
Abstract
Theoptimizationofcentrifugalpumpdesignplaysanimportantroleinincreasingtheefficiencyofthepump.Inrecentyears,thecombinationofRBFneuralnetworkandgeneticalgorithmhasbeenusedtooptimizethecentrifugalpumpdesign.ThispaperaimstodiscusstheapplicationofRBFneuralnetworkandgeneticalgorithmintheoptimizationofcentrifugalpumpdesign.TheanalysisoftheperformanceoftheRBFneuralnetworkandgeneticalgorithmhasalsobeencarriedout.
Introduction
Centrifugalpumpsplayanimportantroleindifferentsectorssuchasagriculture,industry,healthcare,andwarindustry.Poorlydesignedcentrifugalpumpsnegativelyaffecttheirefficiencyresultinginanover-consumptionofpowerandpoorperformance.Therefore,effortsaimedatimprovingthedesignofcentrifugalpumpscansignificantlyleadtoincreasedefficiencyandperformance.
Inrecentyears,researchershaveproposeddifferentstrategiesfortheoptimizationofthedesignofcentrifugalpumps.AmongthesestrategiesisthecombinationofRBFneuralnetworkandgeneticalgorithm.ThispaperdiscussestheapplicationoftheRBFneuralnetworkandgeneticalgorithmfortheoptimizationofcentrifugalpumpdesign.
RBFNeuralNetworks
RBFneuralnetworkisalearningsystemthatusestheradialbasisfunctionastheactivationfunction.TheRBFneuralnetworkhasthreelayers,theinputlayer,thehiddenlayer,andtheoutputlayer.Theinputlayerreceivesdatafromtheexternalenvironmentwhilethehiddenlayercomputestheproductofweightsandinputvalues.Theoutputlayerreceivestheproductofhiddenlayervaluesandoutputstherequiredvalue.
TheRBFneuralnetworkiswidelyusedinpatternrecognition,forecasting,andclassification.Intheoptimizationofcentrifugalpumpdesign,theRBFneuralnetworkisusedtopredicttheparametersthataffecttheperformanceofcentrifugalpumps.
GeneticAlgorithm
Geneticalgorithmisanevolutionarystrategythatusesgeneticoperatorstooptimizesolutionstocomplexproblems.Thegeneticalgorithmstartswithaninitialpopulationanditerativelyperformsselection,crossover,andmutation.Selectioninvolvesselectingindividualsthataremostfitfortheproblem.Crossoverinvolvesswappinggeneticmaterialf