Chaotic associative memory for successive learning using internal patterns

Norihiro Kawasaki, Yuko Osana, Masafumi Hagiwara

Research output: Contribution to journalConference article

13 Citations (Scopus)

Abstract

In this paper, we propose a chaotic associative memory for successive learning (CAMSL) using internal patterns. In the CAMSL, the learning process and the recall process are not divided. When an unstored pattern is given to the network, the CAMSL can learn the pattern successively. The CAMSL distinguishes an unstored pattern from the stored patterns. When a stored pattern is given, the CAMSL recalls the pattern. When an unstored pattern is given, the CAMSL changes the internal pattern for the input pattern by chaos and presents the other pattern candidates. When the CAMSL cannot recall the desired pattern, it learns the input pattern as an unstored pattern. We carried out a series of computer simulations and confirmed the effectiveness of the CAMSL.

Original languageEnglish
Pages (from-to)2521-2526
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
Publication statusPublished - 2000 Dec 1
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: 2000 Oct 82000 Oct 11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Chaotic associative memory for successive learning using internal patterns'. Together they form a unique fingerprint.

  • Cite this