Application of wavelets and neural networks to diagnostic sy(7)

卷积神经网络和一些独立成分分析的外文文献

B.H.Chenetal./ComputersandChemicalEngineering23(1999)899–906905

sionisreducedandscale-4%andscale-5%areconsistent.Thereforeusingpiece-wiseprocessingtechnique,itcanachieveconsistentfeatureextractionaswellasdimen-sionreduction.

4.FinalRemarks

Featuresofaprocessdynamictransientsignalareidenti edasthesingularitiesandirregularitiesbecausetheycontainthemostimportantinformationcorre-spondingtochangesofoperationalstates.Theap-

proachdevelopedbyMallatandHwang(1992)andCvetkovicandVetterli(1995)fordeterminingsingulari-tiesandirregularitiesisintroducedforfeatureextrac-tionofdynamictransientsignalsofprocessoperations,whicharetheextremaofwaveletanalysis.Anapproachfornoiseextremaremovalandpiece-wisedimensionreductionarealsodiscussed.Inthesecondpartofthepaper,theuseoftheapproachtoreplacethedatapre-processingpartoftheadaptiveresonancetheorytodevelopamoreef cientunsupervisedandrecursivelearningsystemARTnetanddescribeitsapplicationtoare nery uidcatalyticcrackingprocessisreported.

Fig.6.Noisesignalanditsmulti-resolutionanalysis.Axi,approximationofmultiresolutionanalysis;Dxi,detail.