ON A UNIFIED SYNTHESIZING APPROACH FOR CELLULAR NEURAL NETWORKS

In this paper, we develop a unified synthesizing approach for the cloning templates of Cellular Neural Networks (CNNs). In particular, we shall consider the case when the signal processing problem is complex, and a multi-layered CNN with time-variant templates is necessary. The method originates from the existence of a correspondence between the cloning templates of Cellular Neural Network and its discrete counterpart, Discrete-Time Cellular Neural Network (DTCNN), in solving a prescribed image processing problem when time-variant templates are involved. Thus, one can start with calculating the cloning templates from DTCNN, and then translating the cloning templates to those for CNN operations. As a result, the mathematical tools being used in the synthesis of Discrete-time Cellular Neural Network can also be applied to the analog type Cellular Neural Network. This inevitably helps to simplify the design problem of CNN for signal processing. Examples akin to contour drawing and parallel thinning are shown to illustrate the merits of our proposed method.