Auto-learning by dynamical recognition networks

N. Honma, T. Kamauchi, K. Abe, H. Takeda

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

This paper demonstrates that a new type of hybrid networks can be useful for detecting 'unknown' patterns and auto-learning of them. An essential point of the mechanism is a dynamical recognition based on chaotic EEG activities of mammalian brains. The chaotic activities are generated by designing recurrent connection weights in the hybrid networks with feedforward and recurrent connections. Harnessing the chaotic dynamics of recurrent networks, the networks can recognize 'known' patterns and their neighbors as the conventional recognition methods are possible. We present some simulation results illustrating the networks ability on deciding whether input patterns are 'known' or 'unknown' by observing temporal stability of output patterns. Finally, it is shown that recognition of 'unknown' patterns makes it possible the networks to learn new patterns automatically.

Original languageEnglish
Pages (from-to)III-211 - III-216
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume3
Publication statusPublished - 1999
Event1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn
Duration: 1999 Oct 121999 Oct 15

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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