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时间:2020-05-04
《基于遗传算法改进的BP神经网络模型的磨损机制智能识别-论文.pdf》由会员上传分享,免费在线阅读,更多相关内容在应用文档-天天文库。
1、2014年1月润滑与密封Jan.2014第39卷第1期LUBRICATIONENGINEERINGVo1.39No.1DOI:10.3969/j.issn.0254—0150.2014.01.005基于遗传算法改进的BP神经网络模型的磨损机制智能识别盛晨兴程俊李文明段志和马奔奔(1.高性能船舶技术教育部重点实验室(武汉理工大学)湖北武汉430063;2.武汉理工大学能源与动力工程学院湖北武汉430063;3.西安交通大学润滑理论及轴承研究所陕西西安710049;4.交通运输部南海救助局广东广州510310)摘要:通过提取磨粒形状特征参数、颜色特征参数和表面纹理等特征参数对磨粒形态进行量化表征,
2、并以此为输入矢量,引入遗传算法(GA)改进BP神经网络对磨粒进行自动分类识别,建立遗传算法改进的BP神经网络模型,并给出具体的算法实现过程。分别应用遗传算法改进的BP神经网络模型和未引入遗传算法改进的BP神经网络模型对磨粒图像进行智能识别。实验结果表明,遗传算法改进的BP神经网络综合了遗传算法的全局优化和BP算法局部搜索速度快的特点,网络识别率较高,具有较好的全局性。关键词:特征提取;磨粒识别;遗传算法;BP算法;神经网络中图分类号:THIl7.1;TP301.6文献标识码:A文章编号:0254—0150(2014)1—024—5IntelligentRecognitionofWearMech
3、anismBasedonImprovedBPNeuralNetworkModelbyGeneticAlgorithmShengChenxing,ChengJun,LiWenmingDuanZhihe,MaBenben·(1.KeyLaboratoryofHighPerformanceShipTechnologyofMinistryofEducation(WuhanUniversityofTechnology),WuhanHubei430063,China;2.SchoolofEnergyandPowerEngineering,WuhanUniversityofTechnology,WuhanH
4、ubei430063,China;3.TheoryofLubricationandBearingInstitute,Xi’anJiaotongUniversity,Xi’anShaanxi710049,China;4.NanhaiRescueBureauoftheMinistryofTransport,GuangzhouGuangdong510310,China)Abstract:Aimprovedbackpropagation(BP)neuralnetworkbyGeneticalgorithmwasintroducedtorealizetheauto—maticclassification
5、andrecognitionofweardebris,basedonthequalitativecharacterizationofthemorphologicalfeaturesoftheweardebrismakinguseofthecharacteristicparametersofweardebrisshape,color,andsurfacetexture.Aneuralnet—workmodelbasedontheimprovedbackpropagation(BP)neuralnetworkbyGeneticalgorithmwasestablishedtoclassifyand
6、recognizetheweardebrisusingthoseparametersastheinputvectors.Thealgorithmoftheestablishedmodelwasde—tailed.BycomparingtheresultsofautomaticrecognizingtheweardebrisbytheimprovedBPneuralnetworkandthepres—entedBPneuralnetwork,itshowsthattheimprovedbackpropagation(BP)neuralnetworkcombinestheglobaloptimiz
7、a—\p\qp8tionfeatureofgeneticalgorithmandthefastspeedfeatureinlocalsearchofBPalgorithm,whichhasahighrecognitionrateandbetterglobalsearchfeature.Keywords:characterizationextracting;wearrecognition;Genet
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