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.