Successive learning in hetero-associative memories using chaotic neural networks

Yuko Osana, Masafumi Hagiwara

Research output: Contribution to conferencePaper

1 Citation (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, the data 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 is observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.

Original languageEnglish
Pages1107-1112
Number of pages6
Publication statusPublished - 1998 Jan 1
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: 1998 May 41998 May 9

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period98/5/498/5/9

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ASJC Scopus subject areas

  • Software

Cite this

Osana, Y., & Hagiwara, M. (1998). Successive learning in hetero-associative memories using chaotic neural networks. 1107-1112. Paper presented at Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3), Anchorage, AK, USA, .