Sensitivity Analysis of Kernel Estimates: Implications in Nonlinear Physiological System Identification View Full Text


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Article Info

DATE

1998-05

AUTHORS

David T. Westwick, Béla Suki, Kenneth R. Lutchen

ABSTRACT

Many techniques have been developed for the estimation of the Volterra/Wiener kernels of nonlinear systems, and have found extensive application in the study of various physiological systems. To date, however, we are not aware of methods for estimating the reliability of these kernels from single data records. In this study, we develop a formal analysis of variance for least-squares based nonlinear system identification algorithms. Expressions are developed for the variance of the estimated kernel coefficients and are used to place confidence bounds around both kernel estimates and output predictions. Specific bounds are developed for two such identification algorithms: Korenberg's fast orthogonal algorithm and the Laguerre expansion technique. Simulations, employing a model representative of the peripheral auditory system, are used to validate the theoretical derivations, and to explore their sensitivity to assumptions regarding the system and data. The simulations show excellent agreement between the variances of kernel coefficients and output predictions as estimated from the results of a single trial compared to the same quantities computed from an ensemble of 1000 Monte Carlo runs. These techniques were validated with white and nonwhite Gaussian inputs and with white Gaussian and nonwhite non-Gaussian measurement noise on the output, provided that the output noise source was independent of the test input. More... »

PAGES

488-501

References to SciGraph publications

  • 1986-11. The identification of nonlinear biological systems: Wiener and Hammerstein cascade models in BIOLOGICAL CYBERNETICS
  • 1986-11. The identification of nonlinear biological systems: LNL cascade models in BIOLOGICAL CYBERNETICS
  • 1988-01. Identifying nonlinear difference equation and functional expansion representations: The fast orthogonal algorithm in ANNALS OF BIOMEDICAL ENGINEERING
  • 1993-11. Nonlinear analysis of renal autoregulation under broadband forcing conditions in ANNALS OF BIOMEDICAL ENGINEERING
  • 1978-06. On the choice of noise for the analysis of the peripheral auditory system in BIOLOGICAL CYBERNETICS
  • 1991-07. Dissection of a nonlinear cascade model for sensory encoding in ANNALS OF BIOMEDICAL ENGINEERING
  • 1990-11. The identification of nonlinear biological systems: Wiener kernel approaches in ANNALS OF BIOMEDICAL ENGINEERING
  • 1992-02. Identification of complex-cell intensive nonlinearities in a cascade model of cat visual cortex in BIOLOGICAL CYBERNETICS
  • 1991-07. Parallel cascade identification and kernel estimation for nonlinear systems in ANNALS OF BIOMEDICAL ENGINEERING
  • 1989. Nonlinear Models of Transduction and Adaptation in Locust Photoreceptors in ADVANCED METHODS OF PHYSIOLOGICAL SYSTEM MODELING
  • 1996-03. The identification of nonlinear biological systems: Volterra kernel approaches in ANNALS OF BIOMEDICAL ENGINEERING
  • 1994. Parametric and Nonparametric Nonlinear Modeling of Renal Autoregulation Dynamics in ADVANCED METHODS OF PHYSIOLOGICAL SYSTEM MODELING
  • 1993-11. Identification of nonlinear biological systems using laguerre expansions of kernels in ANNALS OF BIOMEDICAL ENGINEERING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1114/1.40

    DOI

    http://dx.doi.org/10.1114/1.40

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1027935427

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/9570231


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