In　this　paper,　a　fuzzy-membership　cerebellar　model　neural　network　with　differential　evolution　(DEFM-CMNN)　is　proposed,　which　improves　the　prediction　accuracy　of　the　cerebellar　model　neural　network　based　on　fuzzy　membership　(FM-CMNN)　for　prediction　of　time　series.　Firstly,　use　the　linear　quantization　function　of　FM-CMNN　input　space　to　quantize　the　time　series　into　nonlinear　state　variables.　Secondly,　the　mutation,　crossover　and　selection　operations　of　the　differential　evolution　(DE)　algorithm　are　used　to　learn　the　connection　weight　of　the　network.　Meanwhile,　the　adaptive　mutation　operator　is　introduced　to　enhance　the　diversity　of　individuals　in　the　early　stages　of　evolution.　Finally,　the　effectiveness　of　the　proposed　DEFM-CMNN　is　demonstrated　in　experiments　for　time　series　prediction.　The　results　show　that　this　model　has　better　real-time　performance　and　higher　prediction　accuracy.