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简介：ProfessorKowk-faiSo,theeditor-in-chiefofNeuralRegenerationResearch,hasbeennamedaFellowoftheNationalAcademyofInventors(NAI)ProfessorKwok-faiSo,DepartmentofOphthalmology,LiKaShing,FacultyofMedicine,TheUniversityofHongKong(HKU),hasbeennamedaFellowoftheNationalAcademyofInventors(NAI).

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简介：Thispaperproposesanewneuralfuzzyinferencesystemthatmainlyconsistsoffourparts.Thefirstpartisabouthowtouseneuralnetworktoexpresstherelationwithinafuzzyrule.Thesecondpartisthesimplificationofthefirstpart,andexperimentsshowthatthesesimplificationswork.Onthecontrarytothesecondpart,thethirdpartistheenhancementofthefirstpartanditcanbeusedwhenthefirstpartcannotworkverywellinthefuzzyinferencealgorithm,whichwouldbeintroducedinthefourthpart.Finally,thefourthpart"neuralfuzzyinferencealgorithm"isbeenintroduced.Itcaninferencethenewmembershipfunctionoftheoutputbasedonpreviousfuzzyrules.Theaccuracyofthefuzzyinferencealgorithmisdependentonneuralnetworkgeneralizationability.Evenifthegeneralizationabilityoftheneuralnetworkweusedisgood,westillgetinaccurateresultssincethenewcomingrulemaynotberelatedtoanyofthepreviousrules.Experimentsshowthisalgorithmissuccessfulinsituationswhichsatisfytheseconditions.

简介：ThetypicalBDI(beliefdesireintention)modelofagentisnotefficientlycomputableandthestrictlogicexpressionisnoteasilyapplicabletotheAUV(autonomousunderwatervehicle)domainwithuncertainties.Inthispaper,anAUVfuzzyneuralBDImodelisproposed.Themodelisafuzzyneuralnetworkcomposedoffivelayers:input(beliefsanddesires),fuzzification,commitment,fuzzyintention,anddefuzzificationlayer.Inthemodel,thefuzzycommitmentrulesandneuralnetworkarecombinedtoformintentionsfrombeliefsanddesires.ThemodelisdemonstratedbysolvingPEG(pursuit-evasiongame),andthesimulationresultissatisfactory.

简介：NETWORKTELECOMmagazine,commencedpublicationin1999,isthenetwork&telecommunicationsindust0?sleadingpublicationinChina.specializinginthefieldofopticfibercommunications,accessnetwork,mobilecommunications,CATVnetwork,cablingsystemanddatacommunications;andhasestablisheditsrepuionbyacombinationofauthoritativeeditorialcontent,targetingackcuatlonofhigho.ualitycoveringtheentireChinaregiom

简介：TheEuropeanGeoparksNetworkwasestablishedinJune2000byfourregionsofdifferentEuropeanCountries--France,Germany,SpainandGreece--withsimilarnaturalandsocioeconomiccharacteristics.Thesefourregionsareruralareas,withaparticulargeologicalheritage,naturalbeautyandhighculturalpotential,allfacingproblemsofsloweconomicdevelopment,

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简介：NETWORKTELECOMmagazinecommencedpublicationin1999,isthenetwork

简介：NETWORKTELECOMmagazineisthenetwork&telecommunicationsindustry'sleadingpublicationinChina,specializinginthefieldofopticfibercommunications,accessnetwork,mobilecommunications,CATVnetwork,cablingsystemanddatacommunications;andhasestablisheditsreputationbyacombinationofauthoritativeeditorialcontent,targetingacirculationofhighqualitycoveringtheentireChinaregion.

简介：TheBIONISnetworkwassetupinspring2002byProf.GeorgeJeronimidis,Prof.JulianVincentandPhilSheppard.Membershipisnowover300,withmembersfromacademiaandindustryinmorethan40countries.ThemissionofthenetworkistopromotetheapplicationofBiomimeticsinproductsandservicesanditsuseineducationandtraining.ItiscurrentlysupportedbySwedishBiomimetics3000?andhostedbytheUniversityofReading.

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简介：Inthispaper,weproposetwoweightedlearningmethodsfortheconstructionofsinglehiddenlayerfeedforwardneuralnetworks.Bothmethodsincorporateweightedleastsquares.Ourideaistoallowthetraininginstancesnearertothequerytoofferbiggercontributionstotheestimatedoutput.Byminimizingtheweightedmeansquareerrorfunction,optimalnetworkscanbeobtained.Theresultsofanumberofexperimentsdemonstratetheeffectivenessofourproposedmethods.

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简介：Inthispaper,weintroduceatypeofapproximationoperatorsofneuralnetworkswithsigmodalfunctionsoncompactintervals,andobtainthepointwiseanduniformestimatesoftheapproximation.Toimprovetheapproximationrate,wefurtherintroduceatypeofcombinationsofneuralnetworks.Moreover,weshowthatthederivativesoffunctionscanalsobesimultaneouslyapproximatedbythederivativesofthecombinations.Wealsoapplyourmethodtoconstructapproximationoperatorsofneuralnetworkswithsigmodalfunctionsoninfiniteintervals.

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