Abstract
It is necessary to perform the system identification under severe numerical conditions in many practical applications. When less external test signals are available for parameter estimation from experimental data, the identification performance often suffers from numerical problems in the optimization procedure due to the less independent informative components, the influence of complicated noise, or the local minima problem. In this paper, a multi-point search based identification algorithm is investigated for system identification under severe numerical conditions. It introduces the output over-sampling scheme to collect the experimental input-output data, and extracts the information in time and space domains to complement information criterion for numerical optimization. Furthermore, the multi-point search is utilized to decrease the influence of local minima. The numerical simulation examples illustrate that the identification performance has been improved in the proposed algorithm.
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