Gauss Newton variable forgetting factor recursive least squares for time varying parameter tracking
Gauss Newton variable forgetting factor recursive least squares for time varying parameter tracking
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DOI：
10.1049/el:20000727
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发表时间：
2000-05-25
影响因子：
1.1
通讯作者：
Sung, KM
中科院分区：
文献类型：
--
作者：
Song, SW;Lim, JS;Sung, KM
提出了高斯-牛顿变遗忘因子递推最小二乘（GN-VFF-RLS）算法，用于改善时变参数估计的跟踪性能。与已有的带最优遗忘因子的指数加窗递归最小二乘（EW-RLS）算法相比，该方法在快速时变参数估计方面有显著的改进。信噪比和非平稳性的影响进行了测试，使用计算机模拟与给定的参数模型。每个算法的性能评估提出的均方差（MSD）。
The Gauss-Newton variable forgetting factor recursive least squares (GN-VFF-RLS) algorithm is presented, which can be used to improve the tracking capability in time varying parameter estimation. Compared to the existing algorithm, the exponentially windowed recursive least squares (EW-RLS) algorithm with optimal forgetting factor, the presented method leads to a significant improvement in fast time varying parameter estimation. The effects of signal to noise ratio and nonstationarity have been tested using computer simulations with the given parameter model. An assessment of the performance of each algorithm is presented in terms of the mean-square-deviation (MSD).