This　paper　aims　to　present　a　novel　efficient　scheme　in　order　to　more　effectively　control　the　multiple　input　and　multiple　output　(MIMO)　uncertain　nonlinear　systems.　A　wavelet　fuzzy　brain　emotional　learning　controller　(WFBELC)　model　is　proposed,　which　is　comprises　the　benefit　of　wavelet　function,　fuzzy　theory　and　brain　emotional　neural　network.　When　it　is　used　as　the　main　tracking　controller　for　a　MIMO　uncertain　nonlinear　systems,　the　performances　of　the　system,　such　as　the　approximation　ability,　the　learning　performance　and　the　convergence　rate,　will　be　effectively　improved.　Meanwhile,　the　gradient　descent　method　is　used　to　adjust　the　parameters　online　of　WFBELC　and　the　Lyapunov　function　is　employed　to　guarantee　the　rapid　convergence　of　the　control　systems.　For　the　sake　of　the　further　illustrating　the　superiority　of　this　model,　two　examples　of　uncertain　nonlinear　systems,　a　Duffing-Holmes　chaotic　system　and　a　Chua＇s　chaotic　circuit,　are　studied.　After　compared　with　other　models,　the　test　results　show　that　the　proposed　model　can　be　applied　to　obtain　more　satisfactory　control　performance　and　be　more　suitable　to　deal　with　the　influence　of　the　uncertainty　of　the　MIMO　nonlinear　systems.