Stability analysis of high-order Hopfield type neural networks with uncertainty

In this paper, the stability of high-order Hopfield type neural networks with uncertainty is analyzed, the parametric uncertainty is assumed to be bounded. The equilibrium point position may exist for any particular unknown parameter vector in the parameter space, every time one or more of the uncertainty parameters is changed, the equilibrium may shift to a new position or altogether disappear. In the framework of parametric stability, some sufficient conditions are established to guarantee the existence of a globally asymptotically stable equilibrium point for all admissible parametric uncertainties, and the region about the equilibrium point of the nominal part of the neural network that contains the equilibria for each parameter vector in the given subset of the parameter space be estimated.