An optimization approach to design of cellular neural networks

Abstract

In this paper, we consider the problem of realizing associative memories via cellular neural networks (CNNs). After introducing qualitative properties of the CNN model, we formulate the synthesis of CNNs that can store given binary vectors with improved performance as a constrained optimization problem. Next, we observe that this problem's constraints can be transformed into simple inequalities involving linear matrix inequalities. Finally, we reformulate the synthesis problem as a generalized eigenvalue problem, which can be efficiently solved by recently developed interior point methods. The validity of the proposed approach is illustrated by a design example.