Non-fragile chaotic synchronization for discontinuous neural networks with time-varying delays and random feedback gain uncertainties

This paper is concerned with the non-fragile synchronization issue for neural networks with discontinuous activation functions, time-varying delays and random feedback gain uncertainties, where the randomly occurring phenomena are modeled by stochastic variables satisfying the Bernoulli distribution. The appropriate non-fragile controllers are designed to ensure that the global synchronization can be achieved easily. Under the extended Filippov differential inclusion framework, by applying non-smooth analysis theory with a generalized Lyapunov-Krasovskii functional with multiple integral terms and Wirtinger-based multiple integral inequality analysis technique, the global asymptotical stochastic stability of the synchronization error dynamical system is analytically proved, and the non-fragile synchronization conditions are addressed in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to demonstrate the feasibility of the proposed non-fragile controller and the validity of the theoretical results.