Robustness and perturbation analysis of a class of nonlinear systems with applications to neural networks

Studies the robustness properties of a large class of nonlinear systems by addressing the following question: given a nonlinear system with specified asymptotically stable equilibria, under what conditions will a perturbed model of the system possess asymptotically stable equilibria that are close (in distance) to the asymptotically stable equilibria of the unperturbed system? In arriving at the results, the authors establish robustness stability results for the perturbed systems considered, and determine conditions that ensure the existence of asymptotically stable equilibria of the perturbed system that are near the asymptotically stable equilibria of the original unperturbed system. These results involve quantitative estimates of the distance between the corresponding equilibrium points of the unperturbed and perturbed systems. The authors apply the above results in the qualitative analysis of a large class of artificial neural networks