Neural associative memory for intelligent information processing

Motonobu Hattori, Masafumi Hagiwara

Research output: Contribution to conferencePaper

Abstract

In this paper, first we derive a novel relaxation method for the system of linear inequalities and apply it to the learning for associative memories. Since the proposed intersection learning can guarantee the recall of all training data, it can greatly enlarge the storage capacity of associative memories. In addition, it requires much less weights renewal times than the conventional methods. We also propose a multimodule associative memory which can be learned by the intersection learning algorithm. The proposed associative memory can deal with many-to-many associations and it is applied to a knowledge processing task. Computer simulation results show the effectiveness of the proposed learning algorithm and associative memory.

Original languageEnglish
Pages377-386
Number of pages10
Publication statusPublished - 1998 Dec 1
EventProceedings of the 1998 2nd International Conference on knowledge-Based Intelligent Electronic Systems (KES '98) - Adelaide, Aust
Duration: 1998 Apr 211998 Apr 23

Other

OtherProceedings of the 1998 2nd International Conference on knowledge-Based Intelligent Electronic Systems (KES '98)
CityAdelaide, Aust
Period98/4/2198/4/23

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Hattori, M., & Hagiwara, M. (1998). Neural associative memory for intelligent information processing. 377-386. Paper presented at Proceedings of the 1998 2nd International Conference on knowledge-Based Intelligent Electronic Systems (KES '98), Adelaide, Aust, .