Properties of predictor based on relative neighborhood graph localized FIR filters

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    Abstract

    A time signal prediction algorithm based on relative neighborhood graph (RNG) localized FIR filters is defined. The RNG connects two nodes, of input space dimension D, if their lune does not contain any other node. The FIR filters associated with the nodes, are used for local approximation of the training vectors belonging to the lunes formed by the nodes. The predictor training is carried out by iteration through 3 stages: initialization of the RNG of the training signal by vector quantization, LS estimation of the FIR filters localized in the input space by RNG nodes and adaptation of the RNG nodes by equalizing the LS approximation error among the lunes formed by the nodes of the RNG. The training properties of the predictor is exemplified on a burst signal and characterized by the normalized mean square error (NMSE) and the mean valence of the RNG nodes through the adaptation
    Original languageEnglish
    Title of host publicationProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
    VolumeVolume 5
    PublisherIEEE
    Publication date1995
    Pages3391-3394
    ISBN (Print)07-80-32431-5
    DOIs
    Publication statusPublished - 1995
    EventIEEE International Conference on Acoustics, Speech, and Signal Processing 1995 - Detroit, MI, United States
    Duration: 9 May 199512 May 1995

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing 1995
    CountryUnited States
    CityDetroit, MI
    Period09/05/199512/05/1995

    Bibliographical note

    Copyright: 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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