A complex-valued time varying zeroing neural network model for synchronization of complex chaotic systems

Zeroing neural network (ZNN) is an effective tool for solving the synchronization problem of chaotic systems and has received widespread attention. However, previous ZNN models only focused on the synchronization problem of real chaotic systems, failing to tackle the synchronization problem of complex chaotic systems. Based on this, this paper proposes a novel complex-valued time varying zeroing neural network (CVTVZNN) model. This paper rigorously derives the fixed-time convergence and external noise suppression ability of the CVTVZNN model in solving the synchronization problem of complex chaotic systems. Through the results of three numerical experiments on the synchronization of complex Chen chaotic system, complex autonomous chaotic system and complex dynamos chaotic system, verified that the CVTVZNN model has faster convergence speed and higher accuracy in suppressing disturbances compared to other existing ZNN models. It is worth noting that the experimental results show that the CVTVZNN model only takes about 0.00115 s to complete the synchronization task, whether in the noise-free or in environments with external noise. However, in the same experimental environment, other models either cannot achieve synchronization at all, or require at least 0.217 s, which is at least 188 times the synchronization time achieved by the CVTVZNN model. Furthermore, its practical application value has been further demonstrated through its implementation on field programmable gate array.