DESIGN TECHNIQUE OF CELLULAR NEURAL-NETWORK

The cellular neural network (CNN) is composed of a planar placement of cells which consists of a (piecewise-linear) nonlinear element and co ntrolled current sources. It features a simple structure which is clos e to that of the retina and is expected to be utilized in pattern reco gnition and image processing. In CNN, each cell is connected to the ne ighborhood cells by the same pattern, and by adjusting the connection pattern, CNN with various functions can be designed. The connection pa ttern is called the cloning template. It is very important in the deve lopment of new CNN to establish the design method for the cloning temp late. In this paper, the operational characteristics are assumed so th at the output for the normative input satisfies the specified conditio ns. The constraint is derived from the specification and the template is designed so that the corresponding cost function is minimized. The simplex method is used as the optimization technique, which features a simple algorithm. As application examples, noise-remover CNN as well as the maze-tracing CNN are designed and satisfactory results are obta ined. The design method is reported in this paper.