For　high-speed　trains,　high　precision　of　train　positioning　is　important　to　guarantee　train　safety　and　operational　efficiency.　By　analyzing　the　operational　data　of　Beijing–Shanghai　high-speed　railway,　we　find　that　the　currently　used　average　speed　model　(ASM)　is　not　good　enough　as　the　relative　error　is　about　2.5 %.　To　reduce　the　positioning　error,　we　respectively　establish　three　models　for　calculating　train　positions　by　advanced　neural　computing　methods,　including　back-propagation　(BP),　radial　basis　function　(RBF)　and　adaptive　network-based　fuzzy　inference　system　(ANFIS).　Furthermore,　six　indices　are　defined　to　evaluate　the　performance　of　the　three　established　models.　Compared　with　ASM,　the　positioning　error　can　be　reduced　by　about　50 %　by　neural　computing　models.　Then,　to　increase　the　robustness　of　neural　computing　models　and　real-time　response,　online　learning　methods　are　developed　to　update　the　parameters　in　the　last　layer　of　neural　computing　models　by　the　gradient　descent　method.　With　the　online　learning　methods,　the　positioning　error　of　neural　computing　models　can　be　further　reduced　by　about　10 %.　Among　the　three　models,　the　ANFIS　model　is　the　best　in　both　training　and　testing.　The　BP　model　is　better　than　the　RBF　model　in　training,　but　worse　in　testing.　In　a　word,　the　three　models　can　reduce　the　half　number　of　transponders　to　save　the　cost　under　the　same　positioning　error　or　reduce　the　positioning　error　about　50 %　in　the　case　of　the　same　number　of　transponders.　©　2015,　The　Natural　Computing　Applications　Forum.