滑动平均模型的最小二乘辨识方法比较研究

滑动平均模型辩识的困难是信息向量中存在不可测噪声项。借助于递阶辩识的交互估计理论，用估计残差代替信息向量中中不可测噪声项，借助于多新息辨识理论扩展新息长度和充分利用系统观测数据的思想，提出估计滑动平均模型参数的多新息递推最小二乘辨识方法和最小二乘迭代辨识方法。与常规递推增广最小二乘算法相比，提出的方法具有更快的收敛速度，能产生更高精度的参数估计。仿真例子验证了算法的性能。

Comparison of Least Squares Identification for Moving Average Models

Difficulty of identification of moving average models lie in that unknown noise terms appear in the information vector. By means of the interactive estimation theory in hierarchicalidentification-using the estimation residual to replace those noise terms and of the multi-innovation identification theory-expanding the innovation length and making sufficient use of system observation data,a muhi-innovation-recursive least squares and a least-squares iterative algorithms are presented. Compared with the recursive extended least squares algorithms, the proposed two algorithms have fast convergence rates and can produce highly accurate parameter estimation. The simulation results indicate that the proposed alorithms have good performance.