Global Asymptotic Stability of Cohen-Grossberg Neural Networks with Multiple Discrete Delays.

The asymptotic stability is analyzed for Cohen-Grossberg neural networks with multiple discrete delays. The boundedness, differentiability or monotonicity condition is not assumed on the activation functions. The generalized Dahlquist constant approach is employed to examine the existence and uniqueness of equilibrium of the neural networks, and a novel Lyapunov functional is constructed to investigate the stability of the delayed neural networks. New general sufficient conditions are derived for the global asymptotic stability of the neural networks with multiple delays. [ABSTRACT FROM AUTHOR]