Covariate　shift　is　a　critical　issue　of　interval　type-2　fuzzy　neural　networks　(IT2FNNs)　due　to　the　distribution　discrepancy　between　training　and　testing　samples.　In　this　situation,　IT2FNNs　usually　struggle　to　identify　potential　features　from　samples　with　explicit　inductive　biases.　To　address　this　problem,　a　self-organizing　IT2FNN　with　an　adaptive　discriminative　strategy　(ADS-SOIT2FNN)　is　developed　to　maintain　the　identification　performance　in　the　presence　of　covariate　shift.　First,　a　granularity-based　metric　(GM),　using　higher　order　statistics　of　local　samples,　is　designed　to　distinguish　the　distribution　discrepancy　caused　by　covariate　shift.　The　multiple　kernels　incorporated　into　GM　are　able　to　cover　the　sample　features　of　the　whole　Hilbert　space.　Second,　a　self-organizing　strategy,　associated　with　GM-based　discriminative　information,　is　presented　to　alleviate　the　structural　bias　by　growing　and　pruning　fuzzy　rules.　Then,　a　compact　structure　of　ADS-SOIT2FNN　is　achieved　to　adapt　to　the　covariate　shift　of　samples　and　further　strengthen　its　inductive　ability.　Third,　an　adaptive　risk　mitigation　learning　algorithm　(RMLA)　is　introduced　to　update　the　parameters　of　ADS-SOIT2FNN.　RMLA　can　regulate　the　derivatives　of　parameters　with　arbitrary　distribution　samples,　which　is　beneficial　for　maintaining　the　global　accuracy　by　relieving　the　risk　of　parameter　biases.　Finally,　the　effectiveness　of　ADS-SOIT2FNN　is　verified　by　some　experiments　for　identifying　nonlinear　systems　with　covariate　shift.