Literature DB >> 1741529

The interpretation of kernels--an overview.

G K Hung1, L W Stark.   

Abstract

The kernel identification method is a powerful technique for mathematically representing the dynamic behavior of a nonlinear system. This technique has been applied to a number of physical and physiological systems. An important development which has enhanced the usefulness of the kernel method has been the interpretation of the internal structure of a system by examining the shapes of the higher-degree kernels. Examples of various nonlinear models with known structure are illustrated to show a repertoire of kernel shapes. Variations in parameters of these models result in well-defined changes in the shapes of the kernels. Also, examples are shown of kernels obtained from physiological systems to demonstrate how examination of kernel shapes can lead to accurate predictions of the dynamic behavior of the physiological system. Finally, limitations of the applicable range of the kernel identification method are discussed.

Mesh:

Year:  1991        PMID: 1741529     DOI: 10.1007/bf02584323

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  4 in total

1.  Structural classification of multi-input nonlinear systems.

Authors:  H W Chen; L D Jacobson; J P Gaska
Journal:  Biol Cybern       Date:  1990       Impact factor: 2.086

2.  On the interpretation of kernels. I. Computer stimulation of responses to impulse pairs.

Authors:  G Hung; L Stark; P Eykhoff
Journal:  Ann Biomed Eng       Date:  1977-06       Impact factor: 3.934

3.  White-noise analysis of a neuron chain: an application of the Wiener theory.

Authors:  P Z Marmarelis; K Naka
Journal:  Science       Date:  1972-03-17       Impact factor: 47.728

4.  Wiener G-function analysis as an approach to non-linear characteristics of human pupil light reflex.

Authors:  A Sandberg; L Stark
Journal:  Brain Res       Date:  1968-10       Impact factor: 3.252

  4 in total
  1 in total

1.  The identification of nonlinear biological systems: Volterra kernel approaches.

Authors:  M J Korenberg; I W Hunter
Journal:  Ann Biomed Eng       Date:  1996 Mar-Apr       Impact factor: 3.934

  1 in total

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