Position-based competition learning of neural-networks array

R. Saegusa; P. Hartono; S. Hashimoto

Position-based competition learning of neural-networks array

R. Saegusa^*, P. Hartono, S. Hashimoto

^*この研究の対応する著者

研究成果: Conference contribution

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In this paper, we propose a model of neural-network array composed of a number of MLPs (members), in which each member can be automatically trained to recognize the different dynamics of time series data. The proposed array adopts a position-based competitive learning methods that puts members with similar dynamics close to each other. The proposed array model intends to deal effectively with switching dynamics problems and produce a map of the dynamics.

本文言語	English
ホスト出版物のタイトル	Proceedings of the International Joint Conference on Neural Networks
ページ	2817-2820
ページ数	4
巻	4
出版ステータス	Published - 2001
イベント	International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC 継続期間: 2001 7月 15 → 2001 7月 19

Other

Other	International Joint Conference on Neural Networks (IJCNN'01)
City	Washington, DC
Period	01/7/15 → 01/7/19

ASJC Scopus subject areas

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title = "Position-based competition learning of neural-networks array",

abstract = "In this paper, we propose a model of neural-network array composed of a number of MLPs (members), in which each member can be automatically trained to recognize the different dynamics of time series data. The proposed array adopts a position-based competitive learning methods that puts members with similar dynamics close to each other. The proposed array model intends to deal effectively with switching dynamics problems and produce a map of the dynamics.",

author = "R. Saegusa and P. Hartono and S. Hashimoto",

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language = "English",

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AU - Hartono, P.

AU - Hashimoto, S.

PY - 2001

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AB - In this paper, we propose a model of neural-network array composed of a number of MLPs (members), in which each member can be automatically trained to recognize the different dynamics of time series data. The proposed array adopts a position-based competitive learning methods that puts members with similar dynamics close to each other. The proposed array model intends to deal effectively with switching dynamics problems and produce a map of the dynamics.

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M3 - Conference contribution

AN - SCOPUS:0034850385

VL - 4

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EP - 2820

BT - Proceedings of the International Joint Conference on Neural Networks

T2 - International Joint Conference on Neural Networks (IJCNN'01)

Y2 - 15 July 2001 through 19 July 2001

ER -

Position-based competition learning of neural-networks array

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