Robust synchronization of discontinuous Cohen-Grossberg neural networks: Pinning control approach

In this issue, the robust synchronization for a class of uncertain Cohen-Grossberg neural networks is studied, in which neuron activations are modelled by discontinuous functions(or piecewise continuous functions). Pinning state-feedback and adaptive controllers are designed to achieve global robust exponential synchronization and global robust asymptotical synchronization of drive-response-based discontinuous Cohen-Grossberg neural networks. By applying the theory of non-smooth analysis theory and the method of generalized Lyapunov functional, some criteria are given to show that the coupled discontinuous Cohen-Grossberg neural networks with parameter uncertainties can realized global robust synchronization. Some examples and numerical simulations are also shown to verify the validity of the proposed results.