简介：Biasofring-laser-gyroscope(RLG)changeswithtemperatureinanonlinearway.ThisisanimportantrestrainingfactorforimprovingtheaccuracyofRLG.Consideringthelimitationsofleast-squaresregressionandneuralnetwork,weproposeanewmethodoftemperaturecompensationofRLGbiasbuildingfunctionregressionmodelusingleast-squaressupportvectormachine(LS-SVM).StaticanddynamictemperatureexperimentsofRLGbiasarecarriedouttovalidatetheeffectivenessoftheproposedmethod.Moreover,thetraditionalleast-squaresregressionmethodiscomparedwiththeLS-SVM-basedmethod.TheresultsshowthemaximumerrorofRLGbiasdropsbyalmosttwoordersofmagnitudeafterstatictemperaturecompensation,whilebiasstabilityofRLGimprovesbyoneorderofmagnitudeafterdynamictemperaturecompensation.Thus,theproposedmethodreducestheinfluenceoftemperaturevariationonthebiasoftheRLGeffectivelyandimprovestheaccuracyofthegyroscopeconsiderably.