Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Introduction to the theory of neural computation
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Pattern Recognition by Self-Organizing Neural Networks
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ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Improved Chaotic Associative Memory for Successive Learning
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
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In this paper, we propose a Hetero Chaotic Associative Memory for Successive Learning (HCAMSL) with give up function. The proposed model is based on a Chaotic Associative Memory for Successive Learning (CAMSL). In most of the conventional neural network models, the learning process and the recall process are divided, and therefore need all information to learn 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, most of them can deal with only auto-associations. In contract, the proposed HCAMSL can deal with hetero-associations. In the proposed HCAMSL, the learning process and the recall process are not divided. When an unstored pattern is given to the network, the HCAMSL can learn the pattern successively. We carried out a series of computer experiments and confirmed the effectiveness of the proposed HCAMSL.