Dynamics of Delayed Cohen-Grossberg Neural Networks

This paper studies the boundedness of Cohen-Grossberg neural networks with discrete delays and distributed delays (CGNN). Applying Lyapunov function and linear matrix inequalities technique (LMI), some novel sufficient conditions on the issue of the uniformly ultimate boundness, the existence of an attractor and the globally exponential stability for CGNN are established, which can be easily checked by the effective LMI toolbox in Matlab in practice.