In　this　paper,　a　novel　self-organizing　radial　basis　function　(SORBF)　neural　network　is　proposed　for　nonlinear　identification　and　modeling.　The　proposed　SORBF　consists　of　simultaneous　network　construction　and　parameter　optimization.　It　offers　two　important　advantages.　First,　the　hidden　neurons　in　the　SORBF　neural　network　can　be　added　or　removed,　based　on　the　neuron　activity　and　mutual　information　(MI),　to　achieve　the　appropriate　network　complexity　and　maintain　overall　computational　efficiency　for　identification　and　modeling.　Second,　the　model　performance　can　be　significantly　improved　through　the　parameter　optimization.　The　proposed　parameter-adjustment-based　optimization　algorithm,　utilizing　the　forward-only　computation　(FOC)　algorithm　instead　of　the　traditionally　forward-and-backward　computation,　simplifies　neural　network　training,　and　thereby　significantly　reduces　computational　complexity.　Additionally,　the　convergence　of　the　SORBF　is　analyzed　in　both　the　structure　organizing　process　phase　and　the　phase　following　the　modification.　Lastly,　the　proposed　approach　is　applied　to　model　and　identify　the　nonlinear　dynamical　systems.　Simulation　results　demonstrate　its　effectiveness.　Crown　Copyright　(C)　2012　Published　by　Elsevier　Ltd.　All　rights　reserved.