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CNNhavebecomeaveryeffectivemethodforsolvingmanydifferentimageprocessingandrecognitionproblemsThebasicCNNproposedbyChuaandYang [1,2]inisacontinuous-timenetworkintheformofann-by-mIncomputerscienceandmachinelearning,cellularneuralnetworks(CNN)orcellularnonlinear networks(CNN)areaparallelcomputingparadigmsimilartoneuralnetworks,withthedifferencethatcommunicationisallowedbetweenneighbouringunitsonly TRADITIONALCNN,CNN-UBNANDCNN-MVNCNNhavebeenintroducedin[Chua&Yang()]Thispaperhasinitiatedthedevelopmentofanew fieldinneuralnetworksandtheirapplicationsInthisarticlewehavegivenacloseexpressiontodominating[PDF]Analysis,design,andoptimizationofcellular neuralnetworksSemanticScholarCNNhavebecomeaveryeffectivemethodforsolvingmanydifferentimageprocessingandrecognitionproblemsCellular NeuralNetwork(CNN)isananalogparallelcomputingparadigmdefinedinspace,andcharacterizedbylocalityofconnectionsbetweenprocessingelements (cells,orneurons).ThebasicCNNproposedbyChuaandYang[1,2]inisacontinuous-timenetworkintheformofann-by-mTRADITIONALCNN,CNNUBNANDCNN-MVN.PublishedemberEnvironmentalScience,ComputerScience.DOI:/ETHZ-ACorpusIDAnalysis,design,andThefieldofcellularneural networks(CNNs)isofgrowingimportanceinnonlinearcircuitsandsystemsanditismaturingtothepointofbecominganewareaofstudyingeneralnonlinear theory, CellularNeuralNetworks:DynamicsAndModellingAuthorsandAffiliationsThispaperhasinitiatedthedevelopmentofanewfieldinneural networksandtheirapplicationsSchoolofComputerScienceandEngineering,UniversityofElectronicScienceandTechnologyofChina,Chengdu,,People’s RepublicofChinaDOI:/S(00)CorpusID:;Cellularneuralnetworksandcomputationalintelligenceinmedicalimageprocessing@article{AizenbergCellularNN, title={Cellularneuralnetworksandcomputationalintelligenceinmedicalimageprocessing},author={IgorNAizenbergandNaumNAizenbergandJensHiltner andAbstract.Goldimmunochromatographicstripassayprovidesarapid,simple,single-copyandon-sitewaytodetectthepresenceorabsenceofthetarget analyteSuchsystemsarebestsuitedforlocalanddiffusion-solvableproblemssuchasthoseconsideredaboveTitle:Cellularneuralnetworks:theoryCircuitsand Systems,IEEETransactionsonAuthor:IEEECreatedDate/26/AMIncomputerscienceandmachinelearning,cellularneuralnetworks(CNN)orcellular nonlinearnetworks(CNN)areaparallelcomputingparadigmsimilartoneuralnetworks,withthedifferencethatcommunicationisallowedbetweenneighbouring unitsonlyAcellularneuralnetwork(CNN)isacontinuousordiscrete-timeartificialneuralnetworkthatfeaturesamulti-dimensionalarrayofneuroncellsandlo‐calinterconnectionsamongthecellsCNNsemergedthroughtwoseminalpapersco-authoredbyProfessorLeonOChuabackinSincethen,theattentionthat CNNshaveattractedThemodelofCellularNeuralNetworks(CNNs)wasfirstproposedbyChuaandYang[42,]inChapterPDFAuthorinformation Comparingtothetraditionalqualitativeorsemi-quantitativemethod,acompletelyquantitativeinterpretationofthestripscanleadtomorefunctionalinformation thanthetraditionalAcellularneuralnetwork(CNN)isacontinuousordiscrete-timeartificialneuralnetworkthatfeaturesamulti-dimensionalarrayofneuroncells andlocalinterconnectionsamongthecellsTLDRASlavovaCNNhavebeenintroducedin[Chua&Yang()]TheoremsandThisuniqueundergraduate-level textbookincludesmanyexamplesandexercises,includingCNNsimulatoranddevelopmentsoftwareaccessibleviatheInternet,anidealTheCellularNeural Network(CNN)isacomputerparadigminthefieldofmachinelearningandcomputerscience