Global Exponential Stability of a General Class of Neural Networks with Delays and Impulses
Chaolong Zhang, Fengjian Yang, Dongqing Wu · 2006
By using the methods of variation of constants in ordinary differential equations, piecewise Lyapunov function and the contraction mapping principle in Banach spaces, the exponential stability of a general class of neural networks with delays and impulses is discussed. The existence of a uniqueness of periodic solution for this model is obtained, and several global exponential stability criteria of the equilibrium point are established. Two linear impulsive neural networks with delays are constructed, which can be used to design impulsive controllers to control the exponential stability of the system. This method is possible in practical application.