Extreme conditions for one step convergence of the Hopfield neural network
Edilberto Pereira Teixeira, Fernando A. C. Gomide · 2003
The authors analyze the best and worst conditions of equilibrium for the simplified version of the Hopfield neural network. This analysis can elucidate how the network is able to recognize the learned patterns, and how more complex and detailed analysis can be carried out in a system-theoretic framework. It is shown that, in the worst case, the equilibrium is not guaranteed for a stored pattern. >