Type-2　fuzzy　neural　networks　(T2FNNs)　are　particularly　effective　in　dealing　with　nonlinear　systems.　However,　they　inevitably　suffer　from　multicollinearity　problems　caused　by　the　significant　overlaps　of　the　footprint　uncertainty　(FOU),　which　leads　to　generalization　biases.　To　solve　this　challenge,　a　self-organizing　stacked　T2FNN　with　rule　generalization　(RG-SOST2FNN)　is　developed　to　boost　its　overall　performance.　First,　a　stacked　technique　with　cosine　smart　priority　is　designed　for　T2FNN　fusion.　This　technique　employs　multivariable　cosine　similarity　to　obtain　sparse　inputs,　which　selectively　stacks　multiple　T2FNNs　with　non-collinear　inputs　to　reduce　collinearity　dependence.　Second,　a　dynamic　stacked　framework　with　a　rule　cluster　generation　mechanism　is　developed　to　achieve　individual　and　batch　rule　adjustment.　Then,　a　stacked　structure　with　diversity　is　obtained　to　alleviate　collinearity　among　rules　by　eliminating　the　singularity　of　the　parameter　matrix　of　FOUs.　Third,　a　stacked　risk　mitigation　algorithm　is　proposed　to　shape　the　fuzzy　rule　clusters　(FRCs).　Then,　the　parameters　of　FRCs　are　optimized　using　sparse　gradient　learning,　which　avoids　the　updating　of　collinear　features　to　reduce　the　variance　of　parameter　estimation.　Finally,　the　simulation　tests　show　that　RG-SOST2FNN　can　achieve　state-of-the-art　performance　even　at　high　multicollinearity　in　complex　systems.