New criteria on global robust stability of CohenGrossberg neural networks with time-varying delays ?
This paper considers the problem of robust stability analysis of CohenGrossberg neural networks with time-varying delays and norm-bounded parameter uncertainties. The activation functions are assumed to be bounded and globally Lipschitz continuous. Both the monotonic increasing and non-monotonic increasing activations are considered. In terms of linear matrix inequalities (LMIs), sufficient conditions are obtained by using the LyapunovKrasovskii method, which guarantee the existence, uniqueness and global robust asymptotic stability of the equilibrium point of the delayed CohenGrossberg neural network. It is theoretically established that the derived LMI conditions are less conservative than certain existing ones in the literature. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI conditions.
robust stability cohengrossberg neural networks neural networks time-varying delays linear matrix inequality global asymptotic stability