A non-uniform discrete-time cellular neural network and its stability analysis

In this study, we discuss a discrete-time cellular neural network (DTCNN) and its applications including convergence property and stability. Two theorems about the convergence condition of nonreciprocal non-uniform DTCNNs are described, which cover those of reciprocal one as a special case. Thus, it can be applied to wide classes of image processings, such as associative memories, multiple visual patterns recognition and others. Our DTCNN realized by the software simulation can largely reduce the computational time compared to the continuous-time CNN.