A　novel　learning　algorithm　is　proposed　for　nonlinear　modelling　and　identification　using　radial　basis　function　neural　networks.　The　proposed　method　simplifies　neural　network　training　through　the　use　of　an　adaptive　computation　algorithm　(ACA).　In　addition,　the　convergence　of　the　ACA　is　analyzed　by　the　Lyapunov　criterion.　The　proposed　algorithm　offers　two　important　advantages.　First,　the　model　performance　can　be　significantly　improved　through　ACA,　and　the　modelling　error　is　uniformly　ultimately　bounded.　Secondly,　the　proposed　ACA　can　reduce　computational　cost　and　accelerate　the　training　speed.　The　proposed　method　is　then　employed　to　model　classical　nonlinear　system　with　limit　cycle　and　to　identify　nonlinear　dynamic　system,　exhibiting　the　effectiveness　of　the　proposed　algorithm.　Computational　complexity　analysis　and　simulation　results　demonstrate　its　effectiveness.