Search results for "fuzzy neural network"

showing 3 items of 3 documents

USE OF FUZZY NEURAL NETWORKS IN MODELING RELATIONSHIPS OF HPV INFECTION WITH APOPTOTIC AND PROLIFERATION MARKERS IN POTENTIALLY MALIGNANT ORAL LESIONS

2005

To evaluate in oral leukoplakia the relationship between HPV infection and markers of apoptosis (bcl-2, survivin) and proliferation (PCNA), also conditionally to age, gender, smoking and drinking habits of patients, by means of Fuzzy neural networks (FNN) system 21 cases of oral leukopakia, clinically and histologically diagnosed, were examined for HPV DNA presence, bcl-2, survivin and PCNA expression. HPV DNA was investigated in exfoliated oral mucosa cells by nested PCR (nPCR: MY09-MY11/GP5-GP6), and the HPV genotype determined by direct DNA sequencing. All markers were investigated by means of standardised immunohistochemistry procedure. Data were analysed by chi-square test, crude OR an…

MaleCancer ResearchOral precancerous lesionSurvivinFuzzy neural networksApoptosisPolymerase Chain ReactionInhibitor of Apoptosis Proteinslaw.inventionlawGenotypePapillomaviridaePolymerase chain reactionLeukoplakiabiologySmokingHPV infectionvirus diseasesMiddle Agedfemale genital diseases and pregnancy complicationsNeoplasm ProteinsCell Transformation NeoplasticProto-Oncogene Proteins c-bcl-2OncologyCarcinoma Squamous CellFemaleMouth NeoplasmsLeukoplakia OralOral SurgeryMicrotubule-Associated ProteinsAdultHPVFuzzy LogicProliferating Cell Nuclear AntigenSurvivinCarcinomamedicineHumansBcl-2AgedCell ProliferationAnalysis of VariancePapillomavirus InfectionsMouth Mucosamedicine.diseaseProliferating cell nuclear antigenDNA ViralImmunologybiology.proteinCancer researchNeural Networks ComputerNested polymerase chain reaction
researchProduct

Expression of cell cycle markers and human papillomavirus infection in oral squamous cell carcinoma: use of fuzzy neural networks.

2005

Our aim was to evaluate in oral squamous cell carcinoma (OSCC) the relationship between some cell cycle markers and HPV infection, conditionally to age, gender and certain habits of patients, and to assess the ability of fuzzy neural networks (FNNs) in building up an adequate predictive model based on logic inference rules. Eighteen cases of OSCC were examined by immunohistochemistry for MIB-1, PCNA and survivin expression; presence of HPV DNA was investigated in exfoliated oral mucosa cells by nested PCR (nPCR, MY09-MY11/GP5-GP6), and HPV genotype was determined by direct DNA sequencing. Data were analyzed by traditional statistics (TS) and FNNs. HPV DNA was found in 9/18 OSCCs (50.0 %) wi…

OncologyMaleCancer ResearchSurvivinmedicine.disease_causeInhibitor of Apoptosis ProteinsRisk FactorsOral mucosaPapillomaviridaeAged 80 and overCell CycleSmokingHPV infectionAge FactorsAnatomical pathologyCell cycleMiddle AgedImmunohistochemistryNeoplasm Proteinsoral squamous cell carcinomamedicine.anatomical_structureCell Transformation NeoplasticOncologyCarcinoma Squamous CellImmunohistochemistryFemaleMouth NeoplasmscarcinogenesisMicrotubule-Associated ProteinsAdultmedicine.medical_specialtyBiologySex FactorsFuzzy LogicInternal medicineSurvivinmedicineHumanshuman papillomaviruAgedfuzzy neural networkGene Expression ProfilingPapillomavirus Infectionsmedicine.diseaseProliferating cell nuclear antigenstomatognathic diseasesImmunologyDNA Viralbiology.proteinNeural Networks ComputerCarcinogenesisInternational journal of cancer
researchProduct

Adaptive variable structure fuzzy neural identification and control for a class of MIMO nonlinear system

2013

This paper presents a novel adaptive variable structure (AVS) method to design a fuzzy neural network (FNN). This AVS-FNN is based on radial basis function (RBF) neurons, which have center and width vectors. The network performs sequential learning through sliding data window reflecting system dynamic changes, and dynamic growing-and-pruning structure of FNN. The salient characteristics of the AVS-FNN are as follows: (1) Structure-learning and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori. The structure-learning approach relies on the contribution of the size of the output. (2) A set of fuzzy r…

fuzzy neural networkArtificial neural networkNeuro-fuzzyComputer Networks and CommunicationsApplied MathematicsProcess (computing)Fuzzy logicWeightingControl and Systems EngineeringControl theorySignal ProcessingA priori and a posterioriRadial basis functionSequence learningMathematics
researchProduct