Space-varying cellular neural networks designed by Hopfield neural network

In this study, we propose a space-varying cellular neural network (CNN) designed by Hopfield neural network (Hopfield NN). CNN is classified into two types of system like space-invariant system and space-varying system. Space-invariant means that all cells have identical template. On the other hand, space-varying means that all cells do not have identical template according to the state values of the cell and neighbor cells and so on. The proposed CNN is the space-varying system and it is designed by using an associative memory ability of Hopfield NN. In general, the design of space-varying systems is not easy. However, we can set one of prepared existing templates on each cell of CNN according to the retrieved pattern by Hopfield NN to which some typical local image structures are embedded. Namely, we need only some existing templates and their associated patterns. Some simulation results show the basic properties of the proposed CNN and its effectiveness.