Computers & Mathematics with Applications
Stochastic Exponential Stability for Markovian Jumping BAM Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust global exponential stability of Cohen-Grossberg neural networks with time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
Synchronization control of a class of memristor-based recurrent neural networks
Information Sciences: an International Journal
Mathematical and Computer Modelling: An International Journal
| Hi-index | 7.30 |
For neural networks with all the parameters unknown, we focus on the global robust synchronization between two coupled neural networks with time-varying delay that are linearly and unidirectionally coupled. First, we use Lyapunov functionals to establish general theoretical conditions for designing the coupling matrix. Neither symmetry nor negative (positive) definiteness of the coupling matrix are required; under less restrictive conditions, the two coupled chaotic neural networks can achieve global robust synchronization regardless of their initial states. Second, by employing the invariance principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the robust synchronization of almost all kinds of coupled neural networks with time-varying delay based on the parameter identification of uncertain delayed neural networks. Finally, numerical simulations validate the effectiveness and feasibility of the proposed technique.