A new methodology to the design of associative memories based on cellular neural networks

In this paper, we consider the problem of realizing associative memories via cellular neural networks(CNNs). We formulate the synthesis of CNN that can store given binary vectors with improved performance as a constrained optimization problem. Next, we convert the synthesis problem into a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. The validity of the proposed approach is illustrated by computer simulations.