基于LS-SVM的船舶操纵运动在线建模（英文）-豆柴文库

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基于LS-SVM的船舶操纵运动在线建模（英文）
Title:OnlineModellingofShipManeuveringMotionbasedonLS-SVM
Abstract:
Thestudyofshipmaneuverabilityiscrucialforimprovingsafetyandefficiencyinmaritimetransportation.Real-timeandaccuratemodelingofshipmaneuveringmotionplaysavitalroleinvariousapplicationssuchasshiphandling,navigation,andcontrol.ThispaperproposesanonlinemodelingapproachbasedonLeastSquaresSupportVectorMachine(LS-SVM)toaccuratelyestimateandpredictshipmaneuverabilityinreal-time.
1.Introduction
Shipmaneuveringmotionreferstotheabilityofashiptochangedirectionandspeedefficiently.Itisinfluencedbyvariousexternalfactorssuchaswind,current,andwaveconditions,aswellasinternalfactorssuchaspropulsionsystemandshipdesign.Modelingshipmaneuverabilityischallengingduetothecomplexdynamicsanduncertaintiesinvolved.
2.LS-SVM
LeastSquaresSupportVectorMachine(LS-SVM)isapowerfulmachinelearningalgorithmthatcombinestheadvantagesofSupportVectorMachines(SVM)andleastsquaresregression.LS-SVMhasshownexcellentperformanceinnonlinearmodelingandpredictiontasks.Itcanhandlelargedatasetsandisespeciallysuitableforonlinemodelingapplications.
3.DataCollection
Tobuildanaccurateshipmaneuveringmodel,acomprehensivedatasetofshipmaneuveringmotionisrequired.Thisdatasetcanbecollectedusingvarioussensors,suchasGlobalPositioningSystem(GPS),InertialMeasurementUnits(IMUs),andradar.Thecollecteddatashouldincludeshippositions,velocities,accelerations,andrelevantenvironmentalconditions.
4.FeatureExtraction
BeforefeedingthedataintotheLS-SVMmodel,relevantfeaturesneedtobeextracted.Featurescanbederivedfromshipkinematicvariables,environmentalconditions,andotherrelevantfactorsthataffectshipmaneuverability.FeatureextractiontechniquessuchasPrincipalComponentAnalysis(PCA)andIndependentComponentAnalysis(ICA)canbeemployedtoreducethedimensionalityoftheinputdata.
5.LS-SVMModelTraining
Oncethedatasetandfeaturesareprepared,theLS-SVMmodelcanbetrained.TheLS-SVMmodelseekstofindtheoptimalhyperplanethatmaximallyseparatestheshipmaneuversinthefeaturespace.Thetrainin