When　the　initial　parameters　of　the　fuzzy　cerebellar　model　neural　network　(FCMNN)　are　not　properly　selected,　there　is　a　great　possibility　that　the　error　function　converges　to　the　local　minimum　region　or　diverges　under　the　gradient　descent　back　propagation　(BP)　algorithm,　which　will　affect　the　classification　ability　of　FCMNN.　Aiming　at　this　problem,　GA　is　used　to　optimize　the　initial　center　positions　and　width　of　the　activation　function　and　weight　of　FCMNN.　After　obtaining　the　optimal　initial　parameters,　the　internal　parameters　of　FCMNN　can　approach　the　convergence　value　of　minimum　error　more　quickly　and　accurately　through　further　training　of　the　network,　so　as　to　obtain　better　network　learning　performance.　The　introduction　of　GA　to　optimize　the　initial　value　of　FCMNN　can　effectively　reduce　the　blindness　and　time　cost　of　manual　selection　of　initial　parameters,　and　further　improve　the　intelligence　of　neural　network　diagnosers.　The　simulation　and　experimental　results　show　that　the　classification　ability　of　the　GA-FCMNN　and　GA　can　effectively　find　the　optimal　combination　in　the　set　data　domain.