Associative memory design via perceptron learning

In the present paper, a new synthesis approach is developed for associative memories based on the perceptron learning algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron learning is evident. The perceptron learning in the synthesis algorithms is guaranteed to converge. To demonstrate the applicability of the present results, a specific example is considered.