Successive learning in chaotic neural network

Yuko Osana, Masafumi Hagiwara

Research output: Contribution to conferencePaper

Abstract

In this paper, we propose a successive learning method in a chaotic neural network using a continuous pattern input. It can distinguish an unknown pattern from the stored known patterns and learn the unknown pattern successively. In the proposed model, it makes use of the difference in the response to the input pattern in order to distinguish an unknown pattern from the stored known patterns. When an input pattern is regarded as an unknown pattern, the pattern is memorized. Furthermore, it can estimate and learn a correct pattern from a noisy unknown pattern or an incomplete unknown pattern by considering the temporal summation of the continuous pattern 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
Pages1510-1515
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 chaotic neural network. 1510-1515. Paper presented at Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3), Anchorage, AK, USA, .