Functional　electrical　stimulation　(FES)　has　been　widely　used　in　limb　rehabilitation.　The　first　step　for　the　precision　rehabilition　is　to　clarify　the　variation　of　limb　angle　induced　by　FES.　In　this　study,　an　electric　stimulator　and　an　inertial　sensor　are　used　to　build　a　human　body　experimental　platform.　Motion　characteristics　of　ankle　angle　induced　by　electrical　stimulation　pulse　variation　are　obtained　through　experiment.　The　obtained　ankle　angle　characteristics　are　used　to　train　a　neural　network-based　Hammerstein　(H)　model　and　the　model　parameters　are　identified　by　the　genetic　algorithm,　which　can　effectively　predict　the　ankle　angle　change　induced　by　electrical　stimulation.　The　structural　parameters　of　the　H　model　are　adjusted　according　to　the　normalized　root　mean　square　error　value　(NRMSE)　of　the　training　data.　The　10-fold　cross-validation　is　used　to　verify　the　feasibility　and　effectiveness　of　the　model.　Experimental　results　show　that　the　neural　network-based　H　model　can　effectively　predict　the　output　change　of　the　ankle　angle　induced　by　the　electrical　stimulation　pulse,　and　its　root　mean　square　error　(RMSE)　and　NRMSE　are　2.78　+/-　0.33　degrees　and　23.70　+/-　1.77%,　respectively.　Therefore,　the　proposed　model　can　provide　a　theoretical　basis　for　predicting　ankle　angle　change　in　an　electrical　stimulation　closed-loop　control　system.