Two-stage least squares based iterative identification algorithm for controlled autoregressive moving average (CARMA) systems
Two-stage least squares based iterative identification algorithm for controlled autoregressive moving average (CARMA) systems
复制标题
DOI：
10.1016/j.camwa.2011.12.002
复制
发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
Guoyu Yao;Ruifeng Ding
中科院分区：
文献类型：
--
作者：
Guoyu Yao;Ruifeng Ding
针对受控自回归滑动平均（CARMA）系统，提出了一种基于两阶段最小二乘的迭代辨识算法。其基本思想是将CARMA系统分解为两个子系统，并分别识别每个子系统。由于每个子系统中所涉及的协方差矩阵的维数变小，该算法具有较高的计算效率。仿真结果表明了该算法的有效性。
A two-stage least squares based iterative (two-stage LSI) identification algorithm is derived for controlled autoregressive moving average (CARMA) systems. The basic idea is to decompose a CARMA system into two subsystems and to identify each subsystem, respectively. Because the dimensions of the involved covariance matrices in each subsystem become small, the proposed algorithm has a high computational efficiency. The simulation results indicate that the proposed algorithm is effective.