Abstract
This paper investigates the characteristics of the associative memories of Hopfield networks (HF) and cellular neural networks (CNN). In a Hopfield network, the memories which are to be stored correspond to the extrema of the energy function defined in the network, and in a CNN, they correspond to the asymptotically stable points of the differential equation of the CNN. The associative memory networks of Chinese characters are simulated on a 9×9 matrix network. And it is found that the memory capacity of a CNN is larger than that of an HF by numerical simulations. In a 3×3 matrix network, the characteristics of the associative memories of Hopfield networks and cellular neural networks are examined in details.
