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Least squares type algorithms for identification in the presence of modeling uncertainty

机译：在存在模型不确定性的情况下进行识别的最小二乘类型算法

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摘要

The celebrated least squares and LMS (least-mean-squares) are system identification approaches that are easily implementable, need minimal a priori assumptions, and have very nice identification properties when the uncertainty in measurements is only due to noises and not due to unmodeled behavior of the system. When there is uncertainty present due to an unmodeled part of the system as well, however, the performance of these algorithms can be poor. Here the authors propose a "modified" weighted least squares algorithm that is geared toward identification in the presence of both unmodeled dynamics and measurement disturbances. The algorithm uses very little a priori information and is easily implementable in a recursive fashion. Through an example the authors demonstrate the improved performance of the proposed approach. Motivated by a certain worst-case property of the LMS algorithm, an H/sub /spl infin// estimation algorithm is also proposed for the same objective of identification in the presence of modeling uncertainty.
机译：著名的最小二乘法和LMS（最小均方）是易于实现的系统识别方法，需要最小的先验假设，并且当测量的不确定性仅是由于噪声而不是由于未建模的行为时，具有非常好的识别属性。系统的。但是，当由于系统的未建模部分而存在不确定性时，这些算法的性能也会很差。在这里，作者提出了一种“修改的”加权最小二乘算法，该算法适用于在存在未建模的动力学和测量干扰的情况下进行识别。该算法使用的先验信息很少，并且可以以递归方式轻松实现。通过一个示例，作者证明了所提出方法的改进性能。出于LMS算法的某些最坏情况的性质，还提出了H / sub / spl infin //估计算法，用于在存在模型不确定性的情况下实现相同的识别目标。

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