Fuzzy　neural　network　(FNN)　is　a　structured　learning　technique　that　has　been　successfully　adopted　in　nonlinear　system　modeling.　However,　since　there　exist　uncertain　external　disturbances　arising　from　mismatched　model　errors,　sensor　noises,　or　unknown　environments,　FNN　generally　fails　to　achieve　the　desirable　performance　of　modeling　results.　To　overcome　this　problem,　a　self-organization　robust　FNN　(SOR-FNN)　is　developed　in　this　article.　First,　an　information　integration　mechanism　(IIM),　consisting　of　partition　information　and　individual　information,　is　introduced　to　dynamically　adjust　the　structure　of　SOR-FNN.　The　proposed　mechanism　can　make　itself　adapt　to　uncertain　environments.　Second,　a　dynamic　learning　algorithm　based　on　the　alpha　-divergence　loss　function　(alpha　-DLA)　is　designed　to　update　the　parameters　of　SOR-FNN.　Then,　this　learning　algorithm　is　able　to　reduce　the　sensibility　of　disturbances　and　improve　the　robustness　of　Third,　the　convergence　of　SOR-FNN　is　given　by　the　Lyapunov　theorem.　Then,　the　theoretical　analysis　can　ensure　the　successful　application　of　SOR-FNN.　Finally,　the　proposed　SOR-FNN　is　tested　on　several　benchmark　datasets　and　a　practical　application　to　validate　its　merits.　The　experimental　results　indicate　that　the　proposed　SOR-FNN　can　obtain　superior　performance　in　terms　of　model　accuracy　and　robustness.