Stability analysis of a class of noise perturbed 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 asymptotically stable equilibria of the perturbed neural system that are 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 a reduced-order system instead of the original one and time-dependent external stimuli.
Meyer-Bäse
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University of Florida, Department of Electrical and Computer Engineering, Gainesville 32611-6130, USA. anke@alpha.ee.ufl.edu
D001245:Association Learning; D008568:Memory; D015233:Models, Statistical; D016571:Neural Networks, Computer