The　frequency　conversion　sinusoidal　chaotic　neural　network　(FCSCNN)　cannot　consider　search　accuracy　and　convergence　speed　simultaneously.In　order　to　solve　the　mentioned　problem,　a　novel　self-adaptive　simulated　annealing　(SSA)　strategy　is　proposed　by　analyzing　the　optimization　mechanism　of　the　transiently　chaotic　neural　network　(TCNN)　and　the　existing　annealing　strategy.It　can　give　appropriate　self-feedback　connection　weights　based　on　the　characteristics　of　Lyapunov　exponent.The　reversed　bifurcation,　Lyapunov　exponent　and　annealing　function　evolution　diagram　of　the　chaotic　neuron　are　given　and　the　dynamic　characteristic　is　analyzed.It　shows　that　the　SSA　strategy　can　choose　appropriate　annealing　speed　in　different　stages,　which　can　not　only　make　full　use　of　chaotic　global　searching　ability　but　also　accelerate　convergence　speed.Based　on　the　neuron　model,　a　novel　FCSCNN　with　SSA　strategy　(FCSCNN-SSA)　is　proposed　and　applied　to　nonlinear　function　optimization　and　combinational　optimization　problems.The　simulation　results　show　that:(1)　The　SSA　strategy　can　targeted　choose　the　appropriate　annealing　speed,　which　is　superior　to　other　several　existing　simulated　annealing　methods　for　pertinence　and　adaptability　and　can　be　expanded　to　other　similar　models　with　same　optimization　mechanism;　(2)FCSCNN-SSA　can　converge　with　a　fast　speed　and　search　accuracy　simultaneously　than　TCNN,　TCNN-SEA,　I-TCNN,　NCNN,　BFS-TCNN,　FCSCNN.　©　2019,　Chinese　Institute　of　Electronics.　All　right　reserved.