A topology preserving neural network for nonstationary distributions

Taira Nakajima; Hiroyuki Takizawa; Hiroaki Kobayashi; Tadao Nakamura

A topology preserving neural network for nonstationary distributions

Taira Nakajima, Hiroyuki Takizawa, Hiroaki Kobayashi, Tadao Nakamura

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	1131-1135
Number of pages	5
Journal	IEICE Transactions on Information and Systems
Volume	E82-D
Issue number	7
Publication status	Published - 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

Cite this

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title = "A topology preserving neural network for nonstationary distributions",

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.",

keywords = "Competitive Hebbian learning rule, Law of the jungle mechanism, Neural networks, Nonstationary probability distribution, Self-organizing map",

author = "Taira Nakajima and Hiroyuki Takizawa and Hiroaki Kobayashi and Tadao Nakamura",

year = "1999",

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AU - Nakajima, Taira

AU - Takizawa, Hiroyuki

AU - Kobayashi, Hiroaki

AU - Nakamura, Tadao

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AB - 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.

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KW - Law of the jungle mechanism

KW - Neural networks

KW - Nonstationary probability distribution

KW - Self-organizing map

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A topology preserving neural network for nonstationary distributions

Abstract

Keywords

ASJC Scopus subject areas

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