Improved multidirectional associative memories for training sets including common terms

Abstract

Improved multidirectional associative memories (IMAMs) are proposed and simulated. The IMAM fundamental component is a multilayer neural network. IMAMs can memorize and recall multiple associations even when training sets include common terms, such as the training sets composed of (A,a,1), (A,b,2), (C,b,3). The structure of the proposed IMAMs is represented by mutual connections of multilayer neural networks. The proposed IMAMs require less parameters compared with other associative memories and are capable of automatic recall. Recall performance can be greatly improved by using a priority coefficient.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>