A new synthesis procedure for a class of cellular neural networks with space-invariant cloning template

This paper presents a new synthesis procedure (design algorithm) for cellular neural networks (CNN's) with a 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 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. An algorithm is also provided to design CNN's with space-invariant cloning templates and with symmetric connection matrices to guarantee the global stability of the network. Two specific examples are included to demonstrate the applicability of the methodology developed herein.