Sliding model synchronization controller design for chaotic neural network with time-varying delay

Abstract

In this study, the synchronization of chaotic neural networks with time-varying delay is developed based on parameter identification and sliding model control. Under the framework of master/slave chaotic neural networks, recurrent neural network, is developed to accommodate the on-line synchronization, which the weights of the neural network are iteratively and adaptively updated through the error signals between the master and slave systems. The sliding model synchronization controller designed to satisfy the external disturbance vector with unknown upper bound. To guarantee the correctness, rigorousness, generality of the developed results, Lyapunov stability theory is referred to prove the error system stable. Numerical simulations show the synchronization method worked well.