Global Asymptotic Stability Analysis of Both Matched and Unmatched Uncertain Neural Networks

We establish robustness stability results for a specific type of artificial neural networks for associative memories under parameter perturbations and determine conditions that ensure the existence of global asymptotically stable equilibria of the perturbed neural system that are near the asymptotically stable equilibria of the original unperturbed neural network. The proposed stability analysis tool is the sliding mode control and it facilitates the analysis by considering only the nominal plant under the nonlinear nominal control, which annihilates the matched uncertainties.