Exponential stability and trajectory bounds of neural networks under structural variations

The dynamic behavior of neural networks under arbitrary unknown structural perturbations depends essentially on the compatibility/incompatibility of input variables in these networks. Estimates of the upper bounds of the motions of neural networks of either type and exponential stability of compatible neural networks are established by using three different forms of Lyapunov functions. Conditions for the maximum possible estimate of the domain of structural exponential stability are determined. All new concepts such as compatible/incompatible neural networks and structural exponential stability are defined. All the conditions are stated in simple algebraic forms. Their applications are straightforward.