Autoassociative memory design using interconnected generalized Brain-State-in-a-Box neural networks

Cheolhwan Oh, Stanislaw H. Zak, Guisheng Zhai

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A class of interconnected neural networks composed of generalized Brain-State-in-a-Box (gBSB) neural subnetworks is considered. Interconnected gBSB neural network architectures are proposed along with their stability conditions. The design of the interconnected neural networks is reduced to the problem of solving linear matrix inequalities (LMIs) to determine the interconnection parameters. A method for solving LMIs is devised generating the solutions that, in general, are further away from zero than the corresponding solutions obtained using MATLAB's LMI toolbox, thus resulting in stronger interconnections between the subnetworks. The proposed architectures are then used to construct neural associative memories. Simulations are performed to illustrate the results obtained.

Original languageEnglish
Pages (from-to)181-196
Number of pages16
JournalInternational Journal of Neural Systems
Volume15
Issue number3
DOIs
Publication statusPublished - 2005 Jun 1
Externally publishedYes

Keywords

  • Associative memory
  • Brain-State-in-a-Box (BSB) neural network
  • Interconnected neural networks
  • Linear matrix inequalities

ASJC Scopus subject areas

  • Computer Networks and Communications

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