Bootstrapping Parameter Estimation in Dynamic Systems

摘要

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.