Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems
Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems
复制标题
多输入多输出系统的基于梯度和最小二乘的迭代估计算法
DOI：
10.1177/0959651811409491
复制
发表时间：
2012-02-01
影响因子：
1.6
通讯作者：
Bao, B.
中科院分区：
文献类型：
--
作者：
Ding, F.;Liu, Y.;Bao, B.
系统建模对于研究动态系统的运动规律非常重要。系统模型的参数可以通过辨识方法从测量数据中进行估计。本文分别基于梯度搜索和最小二乘原理，开发了一种基于梯度的和一种基于最小二乘的迭代估计算法，用于从输入 - 输出数据中估计具有有色自回归滑动平均（ARMA）噪声的多输入多输出（MIMO）系统的参数。关键是用上一次迭代中相应的估计值来替换信息向量中包含的未知噪声项和残差。仿真测试结果表明，所提出的算法是有效的。
System modelling is important for studying the motion laws of dynamical systems. Parameters of the system models can be estimated through identification methods from measurement data. This paper develops a gradient-based and a least-squares-based iterative estimation algorithms to estimate the parameters for a multi-input multi-output (MIMO) system with coloured auto-regressive moving average (ARMA) noise from input–output data, based on the gradient search and least-squares principles, respectively. The key is to replace the unknown noise terms and residuals contained in the information vector with their corresponding estimates at the previous iteration. The simulation test results indicate that the proposed algorithms are effective.