A neural architecture for nonlinear adaptive filtering of time series

Nils Hoffmann, Jan Larsen

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    A neural architecture for adaptive filtering which incorporates a modularization principle is proposed. It facilitates a sparse parameterization, i.e. fewer parameters have to be estimated in a supervised training procedure. The main idea is to use a preprocessor which determines the dimension of the input space and can be designed independently of the subsequent nonlinearity. Two suggestions for the preprocessor are presented: the derivative preprocessor and the principal component analysis. A novel implementation of fixed Volterra nonlinearities is given. It forces the boundedness of the polynominals by scaling and limiting the inputs signals. The nonlinearity is constructed from Chebychev polynominals. The authors apply a second-order algorithm for updating the weights for adaptive nonlinearities. Finally the simulations indicate that the two kinds of preprocessing tend to complement each other while there is no obvious difference between the performance of the ANL and FNL
    Original languageEnglish
    Title of host publicationProceedings of the IEEE Workshop Neural Networks for Signal Processing
    Publication date1991
    ISBN (Print)0-7803-0118-8
    Publication statusPublished - 1991
    Event1991 IEEE Workshop on Neural Networks for Signal Processing - Princeton, United States
    Duration: 30 Sept 19911 Oct 1991
    Conference number: 1


    Conference1991 IEEE Workshop on Neural Networks for Signal Processing
    Country/TerritoryUnited States
    Internet address

    Bibliographical note

    Copyright: 1991 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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