Interval　type-2　fuzzy　neural　network　(IT2FNN)　has　attracted　considerable　interest　for　modeling　nonlinear　dynamic　systems　in　recent　years.　However,　this　promising　technique　is　confronting　the　problem　that　constructing　a　suitable　IT2FNN　is　a　potential　challenge　ignored　by　most　researchers.　To　solve　this　problem,　a　self-constructing　interval　type-2　fuzzy　neural　network　(SC-IT2FNN),　based　on　the　cooperative　strategies,　is　proposed　in　this　paper.　The　main　contributions　of　this　paper　are:　First,　a　comprehensive　evaluation　algorithm　(CEA),　cooperating　with　the　parameter　optimization,　is　developed　to　design　the　structure　of　SC-IT2FNN　to　enhance　its　generalization　performance.　Second,　a　hierarchical　optimization　mechanism,　cooperating　with　the　nonlinear　and　linear　parameters　of　SC-IT2FNN,　is　proposed　to　accelerate　its　learning　speed.　Third,　the　convergence　of　SC-IT2FNN　is　theoretically　analyzed　in　detail　to　ensure　its　successful　applications.　Finally,　several　benchmark　nonlinear　systems　and　a　real　application　are　utilized　to　evaluate　the　effectiveness　of　SC-IT2FNN.　The　results　demonstrate　that　our　proposed　SC-IT2FNN　significantly　improve　the　modeling　performance　in　terms　of　high　accuracy　and　compact　structure.　(C)　2019　Elsevier　B.V.　All　rights　reserved.