Design of cellular neural networks with space-invariant cloning template

This paper presents a new synthesis procedure (design algorithm) for cellular neural networks with space-invariant cloning template with applications to associative memories. In the present synthesis procedure, the design problem is formulated as a set of linear inequalities and the inequalities are solved using the well-known perceptron training algorithm. When the desired memory patterns are given by a set of bipolar vectors, it is guaranteed that a cellular neural network with a space-invariant cloning template can be designed using the design algorithm developed herein. A specific example is included to demonstrate the applicability of the methodology developed.