Successive learning in hetero-associative memory using chaotic neural networks.

Y. Osana, M. Hagiwara

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In this paper, we propose a successive learning method in hetero-associative memories, such as Bidirectional Associative Memories and Multidirectional Associative Memories, using chaotic neural networks. It can distinguish unknown data from the stored known data and can learn the unknown data successively. The proposed model makes use of the difference in the response to the input data in order to distinguish unknown data from the stored known data. When input data is regarded as unknown data, it is memorized. Furthermore, the proposed model can estimate and learn correct data from noisy unknown data or incomplete unknown data by considering the temporal summation of the continuous data input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit found by Freeman are observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.

Original languageEnglish
Pages (from-to)285-299
Number of pages15
JournalInternational journal of neural systems
Volume9
Issue number4
DOIs
Publication statusPublished - 1999 Aug

ASJC Scopus subject areas

  • Computer Networks and Communications

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