论文标题
递归最小二乘正方形的收敛性和一致性，可变速率忘记
Convergence and Consistency of Recursive Least Squares with Variable-Rate Forgetting
论文作者
论文摘要
通过最小化二次成本函数来得出具有可变速率遗忘（VRF）的递归最小二乘算法。持续的激发和遗忘因子的界限，VRF给出的最小化器显示以收敛到真实的参数。另外，在持续的激发和嘈杂的测量值下，在噪声与回归器不相关的情况下，给出了条件，在该条件下，VRF给出的最小化器是对真实参数的一致估计器。结果通过涉及突然更改参数的数值示例来说明结果。
A recursive least squares algorithm with variable rate forgetting (VRF) is derived by minimizing a quadratic cost function.Under persistent excitation and boundedness of the forgetting factor, the minimizer given by VRF is shown to converge to the true parameters. In addition, under persistent excitation and with noisy measurements, where the noise is uncorrelated with the regressor, conditions are given under which the minimizer given by VRF is a consistent estimator of the true parameters.The results are illustrated by a numerical example involving abruptly changing parameters.
