Aming　at　the　problem　that　the　Levenberg-Marquardt(LM)　algorithm　can　not　online　train　RBF　network　and　the　problem　in　RBF　network　structure　design　methods,　this　paper　presents　an　online　self-adaptive　RBF　network　structure　design　method　based　on　the　LM　algorithm.　The　ideal　of　sliding　window　and　online　structure　optimization　are　introduced　in　this　algorithm,　the　introduction　of　sliding　window　enables　the　RBF　network　to　be　trained　online　by　the　LM　algorithm,　and　makes　the　RBF　network　more　robust　to　the　changes　of　the　learning　parameters　and　is　easy　to　converge.　The　online　structure　optimization　can　online　self-adaptive　adjust　the　structure　of　RBF　network　based　on　the　information　of　training　errors　and　hidden　unites　to　track　the　time-varying　systems,　which　helps　to　maintain　a　compact　netowrk　and　satisfactory　generation.　Finally,　the　experiment　results　show　the　performance　of　the　proposed　algorithm.　©　2017,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.