In　this　paper,　a　novel　fuzzy　neural　network　structure　for　uncertain　nonlinear　systems　is　proposed.　This　network　is　called　wavelet　Takagi-Sugeno-Kang　(TSK)　fuzzy　cerebellar　model　neural　network,　which　includes　the　framework　of　a　cerebellar　model　neural　network　(CMNN)　and　the　wavelet-function-based　TSK　fuzzy　inference　model.　In　order　to　effectively　solve　the　uncertainty　problem　of　nonlinear　systems,　a　new　structure　is　proposed　where　the　wavelet　function　is　used　in　the　consequent　parts　of　TSK-type　fuzzy　CMNN　instead　of　the　linear　combination　of　the　input　variables　in　the　traditional　TSK　fuzzy　systems.　This　structure　combines　the　advantages　of　the　wavelet　function,　the　CMNN　and　the　TSK　fuzzy　inference　system;　thus,　it　is　a　more　effective　model　for　the　uncertain　nonlinear　systems.　In　order　to　provide　fast　training,　parameter　update　laws　of　the　proposed　model　are　derived　based　on　the　gradient　descent　method　in　which　the　learning-rates　are　online　adapted.　Furthermore　the　Lyapunov　function　is　used　to　analyze　the　convergence　of　the　considered　systems.　Finally,　four　different　types　of　applications　are　applied　to　demonstrate　the　effectiveness　of　the　proposed　model.　The　simulation　comparisons　with　other　neural　network　models　have　verified　the　effectiveness　of　the　new　model.