An analytically transparent neural network for sequence recognition

Marc F.J. Drossaers

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    In this article technical details of a neural network for sequence recognition are presented. The network is powerful enough to simulate finite-state acceptors, while its analysis is much simplified, compared to the standard Hopfield model. Also its processing speed is optimal, and the presence of mixture states is externally controllable. The network is robust under synaptic noise, it has error correcting properties and it can recover from gross input errors
    Original languageEnglish
    Title of host publicationICANN ’93
    Subtitle of host publicationProceedings of the International Conference on Artificial Neural Networks Amsterdam, The Netherlands 13–16 September 1993
    EditorsStan Gielen, Bert Kappen
    Place of PublicationBerlin, Germany
    PublisherSpringer
    Pages396-399
    ISBN (Electronic)978-1-4471-2063-6
    ISBN (Print)978-3-540-19839-0
    DOIs
    Publication statusPublished - 30 Jan 1993
    EventInternational Conference on Artificial Neural Networks, ICANN 1993 - Amsterdam, Netherlands
    Duration: 13 Sep 199316 Sep 1993

    Conference

    ConferenceInternational Conference on Artificial Neural Networks, ICANN 1993
    Abbreviated titleICANN
    CountryNetherlands
    CityAmsterdam
    Period13/09/9316/09/93

    Keywords

    • Input image
    • External input
    • Sequence recognition
    • Temporal image
    • Mixture state

    Fingerprint Dive into the research topics of 'An analytically transparent neural network for sequence recognition'. Together they form a unique fingerprint.

    Cite this