简介:Sinceenteringthetwenty-firstcentury,emergingeconomieshavebeenrisingrapidly.Theirfasteconomicgrowthandincreasedshareintheglobaleconomyhavebecomeoutstandingfeaturesofworldeconomicdevelopment.TheriseofemergingeconomieshasexertedstrongpressureontheWestern-ledglobaleconomy,butitwillnecessitateprotractedeffortsinorder
简介:Therobustexponentialstabilityofalargerclassofdiscrete-timerecurrentneuralnetworks(RNNs)isexploredinthispaper.Anovelneuralnetworkmodel,namedstandardneuralnetworkmodel(SNNM),isintroducedtoprovideageneralframeworkforstabilityanalysisofRNNs.MostoftheexistingRNNscanbetransformedintoSNNMstobeanalyzedinaunifiedway.ApplyingLyapunovstabilitytheorymethodandS-Proceduretechnique,twousefulcriteriaofrobustexponentialstabilityforthediscrete-timeSNNMsarederived.Theconditionspresentedareformulatedaslinearmatrixinequalities(LMIs)tobeeasilysolvedusingexistingefficientconvexoptimizationtechniques.Anexampleispresentedtodemonstratethetransformationprocedureandtheeffectivenessoftheresults.