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 proceedingConference contribution


    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 languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Number of pages4
    Publication statusPublished - 2001
    EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC
    Duration: 2001 Jul 152001 Jul 19


    OtherInternational Joint Conference on Neural Networks (IJCNN'01)
    CityWashington, DC

    ASJC Scopus subject areas

    • Software


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