动态调节模型的最小二乘迭代辨识方法

为了改进参数估计精度,利用递阶辨识的交互估计理论,提出了辨识动态调节模型的最小二乘迭代辨识方法。其基本思想是:在每步迭代计算中,将信息向量中或信息矩阵中不可测噪声项用其估计值代替,而噪声估计值又是用前一次迭代参数估计进行计算的,二者执行了一个递阶计算过程。与流行的递推广义最小二乘算法相比,提出的迭代算法在每一步计算中,同时利用了系统所有量测数据信息,因而具有更高的参数估计精度和更快的收敛速度。进行了仿真计算。

Least-squares-iterative Identification Methods for Dynamical Adjusting Models

In order to improve the parameter estimation accuracy, the interactive estimation theory in the hierarchical identification to present a least-squaresiterative algorithm for dynamical adjusting models is used. The basic idea is to replace the unmeasurable noise terms in the information vector/matrix with their estimation values,which is computed iteratively by using the preceding parameter estimates. Comparing with the popular recursive generalized least squares algorithms, the proposed iterative algorithms make sufficient use of all data at each iteration and thus can produce more accurate parameter estimates and have faster convergence rate. Simulation results are included.