Quick learning for multidirectional associative memories
Motonobu Hattori, Masafumi Hagiwara · 2002
In this paper, a quick learning algorithm for multidirectional associative memories (MAMs) is proposed. With this quick learning algorithm, not only the storage capacity of the MAMs can be improved, but also the recall of all training data can be guaranteed. In addition, several important characteristics of the MAMs such as the relation between the required learning epochs and the number of layers, and the relation between the noise reduction effect and the number of layers are introduced.