In this paper we have introduced and analyzed a new class of adaptive nonlinear filters referred to as partition-based linear (Pl) filters. The operation of these filters is based on partitioning the R/sup N/ observation space defined by a size N moving observation window. Specifically, scaler quantization and vector quantization (VQ) have been used as useful examples to illustrate the concept of partitioning the observation space. Each partition is assigned a corresponding set of filter weights. Given that an observation vector lies in a certain partition, the filter uses the corresponding set of weights and forms an estimate by taking a linear combinations of the observation samples. Hence, the name partition-linear filters. Simulations include a novel approach to estimating response-to-response variations in evoked potentials (EP), buried in the on-going electroencephalogram (EEG).


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    Titel :

    Partition-based filters


    Beteiligte:
    Sarhan, A.M. (Autor:in) / Hardie, R.C. (Autor:in)


    Erscheinungsdatum :

    01.01.1995


    Format / Umfang :

    745394 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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