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Improved chaotic associative memory for successive learning. (English) Zbl 1157.68419
Gammerman, A. (ed.), Artificial intelligence and applications. Machine learning. As part of the 26th IASTED international multi-conference on applied informatics. Calgary: International Association of Science and Technology for Development (IASTED); Anaheim, CA: Acta Press (ISBN 978-0-88986-710-9/CD-ROM). 376-381 (2008).
Summary: In this paper, we propose an Improved Chaotic Associative Memory for Successive Learning (ICAMSL). The proposed 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 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 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/HCAMSLMW.
For the entire collection see [Zbl 1154.68012].
MSC:
68T05 Learning and adaptive systems in artificial intelligence
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