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Iteratively reweighted correlation analysis method for robust parameter identification of multiple-input multiple-output discrete-time systems

机译:多输入多输出离散时间系统鲁棒参数辨识的迭代加权关联分析方法

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摘要

In the engineering practices, the distributions of measurements are non-Gaussian as they contain outliers. As some slight deviations from the Gaussian assumption would probably cause the performance of an estimator to degrade significantly, a novel iteratively reweighted correlation analysis method is proposed for robust parameter estimation of multiple-input multiple-output (MIMO) systems, in the presence of Student's t-noises. The iterative method achieves good robustness and high efficiency by the combination of multivariable correlation analysis and t-distribution based M-estimators. The appropriate updating weights are able to enter into the sample cross-correlation function, so that the heavy tails are lowered, and the impact of outliers is weakened to the greatest extent. Based on the robust finite impulse response models, the identification procedure is then to reconstruct the noise-free estimates to identify the parameters of an MIMO system. The theoretical discussions and simulation results demonstrate that the proposed method works well.
机译:在工程实践中,测量值的分布是非高斯的,因为它们包含异常值。由于与高斯假设的一些细微偏差可能会导致估计器的性能显着降低,因此提出了一种新颖的迭代重加权相关分析方法,该方法用于在存在Student's的情况下对多输入多输出(MIMO)系统进行鲁棒的参数估计T噪声。通过多变量相关分析和基于t分布的M估计器的组合,该迭代方法实现了良好的鲁棒性和高效性。适当的更新权重能够进入样本互相关函数,从而降低了粗尾,并且最大程度地减弱了异常值的影响。然后,基于鲁棒的有限脉冲响应模型,识别过程将重建无噪声估计,以识别MIMO系统的参数。理论讨论和仿真结果证明了该方法的有效性。

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