Intersection learning for bidirectional associative memory

Motonobu Hattori, Masafumi Hagiwara

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

In this paper, we propose Intersection Learning for Bidirectional Associative Memory (ILBAM). The proposed ILBAM is based on a novel relaxation method. A number of computer simulations show the following effectiveness of the proposed ILBAM: (1) It can guarantee the recall of all training pairs. (2) It requires much less weights renewal times than the conventional methods. (3) It becomes more effective in case there are many training pairs to be stored. (4) It is insensitive to the correlation of training pairs. (5) It contributes to the noise reduction effect of the BAM.

Original languageEnglish
Pages555-560
Number of pages6
Publication statusPublished - 1996 Jan 1
EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
Duration: 1996 Jun 31996 Jun 6

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period96/6/396/6/6

ASJC Scopus subject areas

  • Software

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