Fixed-Time Synchronization of Stochastic Complex-Valued Fuzzy Neural Networks with Memristor and Proportional Delays

Since complex-valued neural networks (CVNNs) offer numerous advantages over real-valued neural networks, it is essential to investigate the dynamic behavior of CVNNs. The aim of this paper is to study the fixed-time synchronization problem of complex-valued memristor-based fuzzy cellular neural networks (CVMFCNNs) with proportional delays and stochastic disturbance. To achieve this, we separate the CVNNs into their real and imaginary parts and establish subsystems with real values. We then develop a novel delayed feedback controller to achieve the fixed-time synchronization. By employing the Lyapunov functional method and utilizing some differential inequalities, we propose several new criteria for achieving the stochastic fixed-time synchronization of the CVMFCNNs. Furthermore, we estimate the settling time of stochastic fixed-time synchronization, which can be adjusted to the desired value irrespective of the initial conditions. Finally, a simulation example is presented to demonstrate the validity of the obtained results.