Due　to　the　fact　that　the　conventional　radial　basis　function　(RBF)　neural　network　cannot　change　the　structure　on-line,　a　new　dynamic　structure　RBF　(D-RBF)　neural　network　is　designed　in　this　paper.　D-RBF　is　based　on　the　sensitivity　analysis　(SA)　method　to　analyze　the　output　values　of　the　hidden　nodes　for　the　network　output,　then　the　hidden　nodes　in　the　RBF　neural　network　can　be　inserted　or　pruned.　The　final　structure　of　D-RBF　is　not　too　large　or　small　for　the　objectives,　and　the　convergence　of　the　dynamic　process　is　investigated　in　this　paper.　The　grad-descend　method　for　the　parameter　adjusting　ensures　the　convergence　of　D-RBF　neural　network.　The　structure　of　the　RBF　neural　network　is　self-organizing,　and　the　parameters　are　self-adaptive.　In　the　end,　D-RBF　is　used　for　the　non-linear　functions　approximation　and　the　non-linear　systems　modelling.　The　results　show　that　this　proposed　D-RBF　obtains　favorable　self-adaptive　and　approximating　ability.　Especially,　comparisons　with　the　minimal　resource　allocation　networks　(MRAN)　and　the　generalized　growing　and　pruning　RBF　(GGAP-RBF)　reveal　that　the　proposed　algorithm　is　more　effective　in　generalization　and　finally　neural　network　structure.　Copyright　©　2010　Acta　Automatica　Sinica.　All　right　reserved.