Exponential stability and trajectory bounds of neural networks under structural variations

Compatible/incompatible neural networks and structural exponential stability are defined. It is noted that 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 neural networks of either type and the exponential stability of compatible neural networks are established by using three different forms of Lyapunov functions. Moreover, conditions for the maximum possible estimate of the domain of structural exponential stability are determined. The results obtained are in a form suitable for straightforward applications