Interval criterion for stability analysis of discrete-time neural networks with partial state saturation nonlinearities

A generalization of sufficient conditions for global asymptotic stability of the equilibrium x_e=0 of discrete-time neural networks, described by systems which have saturation nonlinearities on part of the states in the case of interval uncertainties, is considered. When using quadratic form Lyapunov functions, sufficient conditions, based on the positive definite interval matrices, are presented. In order to check this, a recent proposed method for determining the outer bounds of eigenvalues ranges is used. A numerical example, illustrating the applicability of the method suggested, is solved at the end of the paper.