To　enhance　the　performance　of　machine　learning　algorithms,　overcome　the　curse　of　dimensionality,　and　maintain　model　interpretability,　there　are　significant　challenges　that　continue　to　confront　fuzzy　systems　(FS).　Mini-batch　Gradient　Descent　(MBGD)　is　characterized　by　its　fast　convergence　and　strong　generalization　performance.　However,　its　applications　have　been　generally　restricted　to　the　low-dimensional　problems　with　small　datasets.　In　this　paper,　we　propose　a　novel　deep-learning-based　prediction　method.　This　method　optimizes　deep　neural-fuzzy　systems　(ODNFS)　by　considering　the　essential　correlations　of　external　and　internal　factors.　Specifically,　the　Maximal　Information　Coefficient　(MIC)　is　used　to　sort　features　based　on　their　significance　and　eliminate　the　least　relevant　ones,　and　then　the　uniform　regularization　is　introduced,　which　enforces　consistency　in　the　average　normalized　activation　levels　across　rules.　An　improved　novel　MBGD　technique　with　DropRule　and　AdaBound　(MBGD-RDA)　is　put　forward　to　train　deep　fuzzy　systems　for　the　training　of　each　sub-FS　in　a　fashion　of　layer　by　layer.　Experiments　on　several　datasets　show　that　the　ODNFS　can　effectively　balance　the　efficiency,　accuracy,　and　stability　within　the　system,　which　can　be　used　for　training　datasets　of　any　size.　The　proposed　ODNFS　outperforms　MBGD-RDA　and　the　state-of-the-art　methods　in　terms　of　accuracy　and　generalization,　with　fewer　parameters　and　rules.