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Published in:
1995 | OriginalPaper | Chapter
Unsupervised Learning of Temporal Sequences by Neural Networks
Authors : B. Gas, R. Natowicz
Published in: Artificial Neural Nets and Genetic Algorithms
Publisher: Springer Vienna
Included in: Professional Book Archive
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We propose to define a new model of formal neural network. This model extends existing Hopfield networks to process temporal data and achieve a non-supervised learning of them. We propose a learning law to adress in this context the sensitivity to input changes. A spatial representation of network’s temporal activity is given by which learnt sequences can be identified. An example of such a network is given and the results of the simulation are presented.