简介:Thepaperpresentsaneuralnetworkforsolvingaclassofquadraticprogrammingproblems.Theneuralnetworkiscompletelystabletoexactsolutionsandtherearenoparameterstoset.Moreover,noanaloguemultipliersanddividersarerequired,incontrasttoexistingneuralnetwork[3]whichneedsplentyofanaloguemultipliers.
简介:Thebasicprobleminoptimizingcommunicationnetworksistoassignapropercircuitforeachorigindestinationpairinnetworkssoastominimizetheaveragenetworkdelay,andthenetworkoptimalrouteselectionmodelisamulti-constrained0-1nonlinearprogrammingproblem,Inthispaper,anewstochasticoptimizationalgorthm,ImmuneAlgorithm,isappliedtosolvetheoptimizationproblemincommunicationnetworks,AndthebackbonenetworkvBNSischosentoillustratethetechniqueofevaluatingdelayinavirtualnetowrk.Atlast,IAiscomparedwiththeoptimizationmethodincommunicationnetworksbasedonGeneticAlgorithm,andtheresultshowsthatIAisbetterthanGAinglobaloptimumfinding.
简介:Theapplicationofcellularneuralnetworks(CNN)forsolvingpartialdifferentialequations(PDEs)isinvestigatedinthispaper.TwokindsofthePDEs,theheat-conductionequationandPoisson'sdquation,areconsideredtobetypicalexamples.TheycanbecomputedinrealtimebyusingtheCNN,whiletheCNN'shardwareisimplementedbytheintegratedOP-AMP.Theexperimentalresultsshowthatthehardwareperformenceisinagreementwiththatgivenbythecomputersimulation.Therefore,theCNNisanewpowerfultoolforsolvingPDEs.
简介:Thekeyideabehindculturalalgorithmistoexplicitlyacquireproblem-solvingknowledgefromtheevolvingpopulationandinreturnapplythatknowledgetoguidethesearch.Inthisarticle,culturalalgorithm-simulatedannealingisproposedtosolvetheroutingproblemofmobileagent.Theoptimalindividualisacceptedtoimprovethebeliefspace’sevolutionofculturalalgorithmsbysimulatedannealing.Thestepsizeinsearchisusedassituationalknowledgetoguidethesearchofoptimalsolutioninthepopulationspace.Becauseofthisfeature,thesearchtimeisreduced.Experimentalresultsshowthatthealgorithmproposedinthisarticlecanensurethequalityofoptimalsolutions,andalsohasbetterconvergencespeed.Theoperationefficiencyofthesystemisconsiderablyimproved.