Designer networks for time series processing

C Svarer, Lars Kai Hansen, Jan Larsen, Carl Edward Rasmussen

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    The conventional tapped-delay neural net may be analyzed using statistical methods and the results of such analysis can be applied to model optimization. The authors review and extend efforts to demonstrate the power of this strategy within time series processing. They attempt to design compact networks using the so-called optima brain damage (OBD) method. The benefits from compact architectures are three-fold. Their generalization ability is at least comparable,they involve less computational burden, and they are faster to adapt if the environment changes. It is shown that the generalization error of the network may be estimated, without extensive cross-validation, using a modification of Akaike's final prediction error (FPE) estimate (1969)
    Original languageEnglish
    Title of host publicationProceedings of the IEEE-SP Workshop on Neural Networks for Signal Processing
    Publication date1993
    ISBN (Print)07-80-30928-6
    Publication statusPublished - 1993
    Event1993 IEEE Workshop on Neural Networks for Signal Processing - , United States
    Duration: 6 Sept 19939 Sept 1993
    Conference number: 3


    Conference1993 IEEE Workshop on Neural Networks for Signal Processing
    Country/TerritoryUnited States
    Internet address

    Bibliographical note

    Copyright: 1993 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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