Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle
Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle
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DOI：
10.1007/s11071-013-1097-z
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发表时间：
2013-10
影响因子：
5.6
通讯作者：
Qianyan Shen;F. Ding
中科院分区：
文献类型：
--
作者：
Qianyan Shen;F. Ding
本文研究了一类Hammerstein非线性系统的迭代辨识问题，该系统由一个无记忆非线性块和一个线性动态块组成。识别的难点在于Hammerstein非线性系统包含非线性部分参数与线性部分参数的乘积，这就导致了系统参数的不可辨识性。为了获得唯一的参数估计，我们利用关键项分离原理将系统的输出表示为所有系统参数的线性组合，并通过将信息向量中的未知变量替换为其估计，推导出基于梯度的迭代识别算法。仿真结果表明，该算法具有良好的性能。
This paper considers iterative identification problems for a Hammerstein nonlinear system which consists of a memoryless nonlinear block followed by a linear dynamical block. The difficulty of identification is that the Hammerstein nonlinear system contains the products of the parameters of the nonlinear part and the linear part, which leads to the unidentifiability of the parameters. In order to obtain unique parameter estimates, we express the output of the system as a linear combination of all the system parameters by means of the key-term separation principle and derive a gradient based iterative identification algorithm by replacing the unknown variables in the information vectors with their estimates. The simulation results indicate that the proposed algorithm can work well.