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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Pages	2817-2820
Number of pages	4
Volume	4
Publication status	Published - 2001
Event	International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC Duration: 2001 Jul 15 → 2001 Jul 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

Software

Cite this

@inproceedings{0580b76a27054ac1bcf6c3cbbca0d02e,

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",

year = "2001",

language = "English",

volume = "4",

pages = "2817--2820",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "International Joint Conference on Neural Networks (IJCNN'01) ; Conference date: 15-07-2001 Through 19-07-2001",

}

TY - GEN

T1 - Position-based competition learning of neural-networks array

AU - Saegusa, R.

AU - Hartono, P.

AU - Hashimoto, S.

PY - 2001

Y1 - 2001

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=0034850385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034850385&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0034850385

VL - 4

SP - 2817

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

Abstract

Other

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this