In　this　paper,　an　efficient　self-organizing　recurrent　radial　basis　function　neural　network　(RRBFNN),　is　developed　for　nonlinear　system　modeling.　In　RRBFNN,　a　two-steps　learning　approach　is　introduced　during　the　learning　process.　In　the　first　step,　the　objective　is　to　find　the　optimal　set　of　parameters　using　an　improved　Levenberg-Marquardt　(LM)　algorithm.　In　the　second　step,　an　efficient　information-oriented　algorithm　(IOA),　without　any　thresholds,　is　developed　to　optimize　the　structure　of　RRBFNN.　The　hidden　neurons　in　this　IOA-based　RRBFNN　(IOA-RRBFNN)　are　generated　or　pruned　automatically　to　reduce　the　computational　complexity　and　improve　the　generalization　power.　Meanwhile,　a　theoretical　analysis　on　the　learning　convergence　of　IOA-RRBFNN　is　given　in　details.　To　demonstrate　the　merits　of　IOA-RRBFNN　for　modeling　nonlinear　systems,　several　benchmark　problems　and　a　real　world　application　are　present　with　comparisons　against　other　existing　methods.　Some　promising　results　are　reported　in　this　study,　indicating　that　the　proposed　IOA-RRBFNN　performs　prediction　accuracy　in　the　case　of　fast　learning　speed　and　compact　structure.　(C)　2017　Elsevier　B.V.　All　rights　reserved.