Bootstrapping Parameter Estimation in Dynamic Systems

  • Huma Lodhi
  • David Gilbert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6926)

Abstract

We propose a novel approach for parameter estimation in dynamic systems. The method is based on the use of bootstrapping for time series data. It estimates parameters within the least square framework. The data points that do not appear in the individual bootstrapped datasets are used to assess the goodness of fit and for adaptive selection of the optimal parameters.

We evaluate the efficacy of the proposed method by applying it to estimate parameters of dynamic biochemical systems. Experimental results show that the approach performs accurate estimation in both noise-free and noisy environments, thus validating its effectiveness. It generally outperforms related approaches in the scenarios where data is characterized by noise.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Huma Lodhi
    • 1
  • David Gilbert
    • 1
  1. 1.School of Information Systems, Computing and MathematicsBrunel UniversityUK

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