Lyapunov diagonally stable matrices to design cellular neural networks for associative memories

Abstract

Lyapunov diagonally stable matrices are used to design cellular neural networks for associative memories. The proposed technique, which guarantees the global asymptotic stability of the equilibrium point, generates neural circuits where the input data are fed via external inputs, rather than initial conditions. This feature makes the suggested approach particularly suitable for hardware implementation techniques. Simulations results are reported to show the advantages and the usefulness of the proposed design method.