Radial　basis　function　neural　network　(RBFNN)　is　an　effective　algorithm　in　nonlinear　system　identification.　How　to　properly　adjust　the　structure　and　parameters　of　RBFNN　is　quite　challenging.　To　solve　this　problem,　a　distance　concentration　immune　algorithm　(DCIA)　is　proposed　to　self-organize　the　structure　and　parameters　of　the　RBFNN　in　this　paper.　First,　the　distance　concentration　algorithm,　which　increases　the　diversity　of　antibodies,　is　used　to　find　the　global　optimal　solution.　Secondly,　the　information　processing　strength　(IPS)　algorithm　is　used　to　avoid　the　instability　that　is　caused　by　the　hidden　layer　with　neurons　split　or　deleted　randomly.　However,　to　improve　the　forecasting　accuracy　and　reduce　the　computation　time,　a　sample　with　the　most　frequent　occurrence　of　maximum　error　is　proposed　to　regulate　the　parameters　of　the　new　neuron.　In　addition,　the　convergence　proof　of　a　self-organizing　RBF　neural　network　based　on　distance　concentration　immune　algorithm　(DCIA-SORBFNN)　is　applied　to　guarantee　the　feasibility　of　algorithm.　Finally,　several　nonlinear　functions　are　used　to　validate　the　effectiveness　of　the　algorithm.　Experimental　results　show　that　the　proposed　DCIA-SORBFNN　has　achieved　better　nonlinear　approximation　ability　than　that　of　the　art　relevant　competitors.