Stabilization of stochastic recurrent neural networks
Edgar Nelson Sanchez, José P. Pérez · 2003
The paper presents the stabilization of a dynamic neural network disturbed by additive Gaussian noise. This stabilization is achieved using a quadratic Lyapunov function. A control law is derived, which ensures that the neural network state becomes globally uniform ultimately bounded (GUUB) in probability. The applicability of the proposed control law is illustrated by an example.