Auto-associative memory by universal learning networks (ULNs)

K. Shibuta, K. Hirasawa, Takayuki Furuzuki, J. Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose a new auto correlation associative memory using universal learning networks (ULNs). The main purpose of this paper is to realize associative memory by training the network. Although so many useful models have been devised, there are some problems related to associative memory, such as the limitation of storage capacity or too small attractors of stored memories. To solve these problems, we obtain memory network by training network parameters not by calculating them in the conventional methods. Furthermore, we introduce "don't care nodes" into the networks just to enlarge network size and give more flexibility. We could verify that this method improves the memory capacity by computer simulations.

Original languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9810475241, 9789810475246
Publication statusPublished - 2002
Externally publishedYes
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 2002 Nov 182002 Nov 22


Other9th International Conference on Neural Information Processing, ICONIP 2002

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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