Application of wavelets and neural networks to diagnostic system development, 1, feature extraction(2)

卷积神经网络和一些独立成分分析的外文文献

900B.H.Chenetal./ComputersandChemicalEngineering23(1999)899–906

issueishowtoremovetheeffectsofnoisecomponentsandachieveconsistentresultsindifferentscales.ThisisthesubjectofSection4.

2.Previousworkonfeatureextractionofdynamictransients

Thissectionbrie yreviewssomeofthepreviousworkonfeatureextraction.Featureextractionisbasi-callyatransformationofthedatacomposingady-namictrendtoalowerdimensionality.Animportantpropertyofsuchatransformationisthatitisinforma-tionpreserving,thatis,dataisreducedbyremovingredundantcomponentswhilepreserving,insomeopti-malsense,informationwhichiscrucialforpatterndiscrimination.

Someresearchershaveadaptedtheepisoderepresen-tationtechniqueoriginatedbyWilliam(1986)toquali-tativeinterpretationoftransientsignals.JanuszandVenkatasubramanian(1991)developedanepisodeap-proachthatusesnineprimitivestorepresentanyplotsofafunction.Eachprimitiveconsistsofthesignsandthe rstandsecondderivativesofthefunction.There-fore,eachprimitivepossestheinformationaboutwhetherthefunctionispositiveornegative,increasing,decreasing,ornotchangingandtheconcavity.Anepisodeisanintervaldescribedbyonlyoneprimitiveandthetimeintervaltheepisodespans.Atrendisaseriesofepisodesthatwhengroupedtogethercancom-pletelydescribethedynamicfeature.Theapproachautomaticallyconvertson-linesensordatatoqualita-tiveclassi cationtrees.CheungandStephanopoulos(1990)developedaslightlydifferentapproachcalledtriangular-episodethatusesseventrianglecomponentstodescribeadynamictrend.BakshiandStephanopou-los(1994,1996)usedwaveletdecompositionoffunc-tionsindifferentscalesandzero-crossingofwaveletderivativesto ndthein ectionsofdecomposition.Inthisway,episodescanbeidenti edautomaticallybycomputers.Basedonepisodeanalysis,dynamictrendscanbeinterpretedassymbolicrepresentations.Themainideaofdynamictrendinterpretationusingepisodeapproachesistoclassifyatrendsuchasincreasingordecreasingpieces.Thisinterpretationissometimesnotenoughandinadequateinprocessanalysis.Further-more,thereisnonoise lteringinanyoftheepisodebasedapproaches,whichsigni cantlylimitsthetrendrepresentationandidenti cationcapability.