Synchronisation of chaotic neural networks with unknown parameters and random time-varying delays based on adaptive sampled-data control and parameter identification

This study investigates the synchronisation problem of chaotic neural networks with unknown parameters and random time-varying delays. By introducing a stochastic variable with Bernoulli distribution, the neural networks with random time-varying delays is transformed into one with deterministic varying delays and stochastic parameters. A simple and robust adaptive sampled-data controller is designed such that the response system can be synchronised with a drive system with unknown parameters by using suitable parameter identification and the Lyapunov stability theory. The proposed synchronisation criteria are easily verified and do not need to solve any linear matrix inequality. Numerical simulations are carried out to demonstrate the effectiveness of the established synchronisation laws.