Abstract
The least squares (LS) identification algorithm is vulnerable to outliers and has large residual square when the measured data is mixed with impulse noise which obeys symmetrical alpha stable (SaS) distribution, so the least absolute deviation (LAD) is selected as the objective function to get better identification performance when impulse noise exists. And taking the non-difference of least absolute deviation into consideration, we adopt an improved gravitational search algorithm as optimal algorithm to search for optimal solution globally. Then the parameter identification method based on LAD objective function using an improved gravitational search algorithm (LAD-IGSA) is put forward creatively. Simulation results show that the LAD-IGSA method can restrain the influence of impulse noise effectively and achieve higher identification accuracy. Moreover, LAD-IGSA method presents better robustness and bettidentification accuracy than LP method with small data sets.
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