Constructing associative memories using high-order neural networks

Research output: Contribution to journalArticle

Abstract

A class of neural network for constructing associative memories that learn the memory patterns as well as their neighbouring patterns is presented. The network is basically a layer of perceptrons with high-order polynomials as their discriminant functions. A learning algorithm is proposed for the network to learn arbitrary bipolar patterns. The simulation results show that the associative memories implemented in this way achieve a set of desirable characteristics, namely high storage capacity, nearest convergence, and existence of a ‘no decision’ state which attracts indistinguishable inputs. Furthermore, it is also possible to shape the attraction basin of a memory pattern under any metrics definition of distance.

Original languageEnglish
Pages (from-to)1122-1124
Number of pages3
JournalElectronics Letters
Volume28
Issue number12
DOIs
Publication statusPublished - 1992 Jun 4

    Fingerprint

Keywords

  • Memories
  • Neural networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this