On the associative memories in cellular neural networks

Abstract

Cellular neural network (CNN) proposed by Chua and Yang (1988) is a sort of interconnecting network, which consists of regularly arranged units. Various applications of CNN are reported such as a feature extraction of the patterns, an extraction of the edges or corners of a figure, noise exclusion, searching in maze and so forth. CNN is also effective as the associative memory by using a noncloning template. Hopfield network is widely known as the neural network with associative memory function, but not many images can be registered on account, of the restrictions. While in CNN, it is possible to embed many images. A 9/spl times/9 matrix Hopfield network can store at most 6/spl sim/9 images, and the same size CNN can store over 30 images. Although CNN is able to embed many images, some uninvited images are included in the memories. This paper proposes a method to avoid the uninvited memory patterns by using associative mapping.