Improved Chaotic Associative Memory for Successive Learning

T. Ikeya, T. Sazuka, A. Hagiwara, and Y. Osana (Japan)

Keywords

Successive Learning, Associative Memory, Chaotic Neural Network, Give Up Function, Multi-Winner Competition

Abstract

In this paper, we propose an Improved Chaotic Associa tive Memory for Successive Learning (ICAMSL). The pro posed model is based on a Hetero Chaotic Associative Memory for Successive Learning with give up function (HCAMSL) and a Hetero Chaotic Associative Memory for Successive Learning with Multi-Winners competition (HCAMSL-MW) 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 learn in advance. However, in the real world, it is very dif´Čücult 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 ICAMSL, the learning process and the recall process are not divided. When an unstored pattern is given to the network, it can learn the pattern successively, and its storage capacity is larger than that of the conventional HCAMSL/HCAMSL MW.

Important Links:



Go Back