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 Wysłany: Nie 3:33, 08 Maj 2011 Temat postu: franklin and marshall sale Feature extraction base |
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,abercrombie en fitch
Feature extraction based on independent component analysis of complex neural network fault diagnosis
USA20LuisBAlmeida. ICAoflinearandnonlinearmixtureasedonmutualin {ormation. ISTandINESC a 1D,mac cosmetics wholesale, Lis-bon,franklin and marshall sale, PortugalMulti-NeuralNetworksforFaultsDiagnosisBasedonICAFeatureExtractionYangShixiJiaoWeidongWuZhaotong (DepartmentofMechanicalEngineering. ZhejiangUniversity, Hangzhou, 310027, China) Abstract: Artificialneuralnetwork (ANN). especiallytheself-organizingmap (SOM) basedonunsupervisedlearningisakindofexcellentmethodforpatternsclusteringandrecognition. Independentcomponentanslysis (ICA) isapowe, rfultoolforanalyzingnongaussiandata. InICA. theFASTICAbasedonfixed-pointiterationisakindofANNalgorithmwithhigheffi-ciency. whichisspeciallyappropriatetofeatureextractionofmultivariatedataatrealtime. Inthispaper, theFASTICAisfirstlyproposedforfeatureextractionofdifferentmechanicalpatterns (includingnormal,franklin and marshall clothing, gearfaultandloosefoundation) · followedbycertaintypicalANN (forexampleRBFNorSOM), whichimplementsthefinalclassification. BymeansofICAandthefurtherfeatureextractionstrategybasedonresidualmutualinformation (RMI), higherthansecondorderfeaturesembeddedinmulti-channelvibrationmeasurementscanbecapturedeffectively. Thus, mechanicalfaultpatternscanberecog-nizedcorrectly. TheresultsfromcontrastexperimentsshowedthatthecompoundICA-SOMclassifiercanbeconstructedinsimplerway, andclassifyvariousfaultpatternsatconsiderableaccuracy,Abercrombie Fitch uk, bothofwhichimplyitsgreatpotentialinhealthcon-ditionmonitoringofmachines. Keywords: faultdiagnosis; neuralnetwork; independentcomponentanalysis first author : Yang Shixi , male, associate professor , PhD . January 1968 students. Tel : (0571) 87951924; E-mail: yangsx @ zju. edu. cn
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