A topology preserving neural network for nonstationary distributions

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

We propose a learning algorithm for selforganizing neural networks to form a topology preserving map from an input manifold whose topology may dynamically change. Experimental results show that the network using the proposed algorithm can rapidly adjust itself to represent the topology of nonstationary input distributions.

Original languageEnglish
Pages (from-to)1131-1135
Number of pages5
JournalIEICE Transactions on Information and Systems
VolumeE82-D
Issue number7
Publication statusPublished - 1999 Jan 1

Keywords

  • Competitive Hebbian learning rule
  • Law of the jungle mechanism
  • Neural networks
  • Nonstationary probability distribution
  • Self-organizing map

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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