資源描述：
《modelling, identification and stable adaptive control of continuous-time nonlinear dynamical systems using neural networks》由會員上傳分享，免費(fèi)在線閱讀，更多相關(guān)內(nèi)容在學(xué)術(shù)論文-天天文庫。
1、1992ACC/WA2Modelling,IdentificationandStableAdaptiveControlofContinuous-TimeNonlinearDynamicalSystemsUsingNeuralNetworksMariosMPolycarpouandPetrosA.IoannouDepartmentofElectricalEngineering-SystemsUniversityofSouthernCaliforniaLosAngeles.CA90089-2563,U.S.AAbstractTheapproximationcapabilitiesofst
2、aticsigmoidaltypenetworksandofradialbasisfunctionnetworkshavebeenstudiedbyseveralSeveralempiricalstudieshavedemonstratedthefeasibilityofem-researchgroups(seefore.g.[6J-7]).InSection2weusetheseresultsployingneuralnetworksasmodelsofnonlineardynamicalsystems.toshowthataproposedgeneralnetworkconfig
3、urationcomposedofThispaperdevelopstheappropriatemathematicaltoolsforsynthe-staticneuralnetworksanddynamicalcomponents(suchasstablefil-sizingandanalyzingstableneuralnetworkbaedidentificationandters)formsatypeofrecurrentnetworkcapableofapproximatingacontrolschemes.Feedforwardnetwokarchitecturesar
4、ecombinedlargeclamofdynamicalsystems.Moreprecisely,itisshownthatwithdynamicalelements,intheformofstablefilters,toconstructathereexistasetofweightssuchthatforagiveninput,theout-generalrecurrentnetworkconfigurationwhichisshowntobecapableputsoftherealsystemandtheproposedrecurrentneuralnetworkofapp
5、roximatingalargeclamsofdynamicalsystems.Adaptiveiden-modelremainarbitrarilycloseoverafiniteintervaloftime.InSec-tificationandcontrolschemes,basedonneuralnetworkmodels,aretion3wedevelopandanalyzeaneuralnetrkbasedidentificationdevelopedusingtheLyapunovsynthesisapproachwiththeprojectionscheme.TheL
6、yapunovsynthesisapproachisusedtoderiveadapmodificationmethod.Theseschemesareshowntoguaranteestabilitytivelawsforadjustingtheweightsofthenetwork.Theseadaptiveoftheoverallsystem,eveninthepresenceofmodellingerrors.Acru-lawsaremodifiedaccordingtotheprojectionalgorithminordertocialcharacteristicofth
7、emethodsandformulationsdevelopedinthisdealwithmodellingerrorsthatmayariseduetotheinadequacyofpaperisthegeneralityoftheresultswhichallowstheirapplicationthenetworktoapproximatetheunknownnonlinearityevenif"op-tovariousneuralnetworkm
