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

    抄録

    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
    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 152001 7 19

    Other

    OtherInternational Joint Conference on Neural Networks (IJCNN'01)
    CityWashington, DC
    Period01/7/1501/7/19

    ASJC Scopus subject areas

    • ソフトウェア

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