Divided chaotic associative memory for successive learning

Abstract

In this paper, we propose a Divided Chaotic Associative Memory for Successive Learning (DCAMSL). The proposed model is based on the Improved Chaotic Associative Memory for Successive Learning (ICAMSL) and the Divided Chaotic Associative Memory for Successive Learning using Internal Patterns (DCAMSL-IP) which were proposed in order to improve the storage capacity. In most of the conventional neural network models, the learning process and the recall process are divided, and therefore they need all information to learning in advance. However, in the real world, it is very difficult to get all information to learn in advance. So we need the model whose learning and recall processes are not divided. As such model, although some models have been proposed, their storage capacity is small. In the proposed DCAMSL, the learning process and the recall process are not divided and its storage capacity is larger than that of the conventional ICAMSL. © 2009 Springer Berlin Heidelberg.