FIXED-TIME SYNCHRONIZATION OF A REACTION-DIFFUSION BAM NEURAL NETWORK WITH DISTRIBUTED DELAY AND ITS APPLICATION TO IMAGE ENCRYPTION

In this paper, we investigate the fixed-time synchronization of reaction-diffusion BAM neural networks, where both discrete and distributed delays are taken into account. Combining Lyapunov stability theory and several integral inequalities, fixed-time synchronization criteria of master and slave systems are established. Through sensitivity analysis, we find the key controller parameters that have a great influence on the maximum settling time. Using the chaotic sequences generated by the neural network, the color image can be encrypted by the Arnold Cat Map and pixel diffusion. Experiments show that the image encryption algorithm designed in this paper has excellent properties of security and anti-attacking, which meets the requirements for the secure transmission of image information.