Interval　Type-2　fuzzy-neural-network　(IT2FNN)　has　been　widely　used　to　model　nonlinear　systems.　In　current　IT2FNN-based　schemes,　however,　one　of　the　main　drawbacks　is　that　the　structure　of　IT2FNN　is　hard　to　be　determined.　In　this　paper,　a　self-organizing　interval　Type-2　fuzzy-neural-network　(SOIT2FNN)　is　introduced　via　considering　the　structure　adjustment　and　the　parameters　learning　process　simultaneously.　Two　main　contributions　of　SOIT2FNN　are　summarized:　Firstly,　an　intensity　of　information　transmission　algorithm,　which　can　evaluate　the　independent　component　contributions　of　fuzzy　rules,　is　introduced　to　optimize　the　structure　of　SOIT2FNN.　Secondly,　an　adaptive　second-order　algorithm,　which　can　obtain　fast　convergence,　is　developed　to　adjust　the　parameters　of　SOIT2FNN.　To　demonstrate　the　merits　of　SOIT2FNN,　several　benchmark　nonlinear　systems　and　a　real　world　application　are　examined　with　comparisons　against　other　existing　methods.　Moreover,　a　statistical　analysis　of　the　performance　results　indicates　that　the　proposed　SOIT2FNN　performs　better　and　is　more　suitable　for　modeling　nonlinear　systems　than　some　existing　methods.　(c)　2018　Elsevier　B.V.　All　rights　reserved.