New results for Hammerstein system identification
New results for Hammerstein system identification
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Hammerstein 系统识别的新结果
DOI：
10.1109/cdc.1995.479059
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Kameshwar Poollat
中科院分区：
文献类型：
--
作者：
Sundeep Rangant;Greg Wolodkint;Kameshwar Poollat
提出了一种分析和设计Hammerstein模型辨识算法的新方法，该模型由静态非线性和LTI系统组成。作者研究了两个识别问题。在第一个问题中，该系统被激发与白色噪声和LTI系统是FIR，他们找到了一个简单的显式解决方案的最佳参数估计，并表明，对于足够大的数据长度的标准迭代技术全局收敛到这个最佳值。在第二个问题中，LTI系统的状态空间的形式和作者表明，标准的状态空间算法可以很容易地修改，以确定Hammerstein模型。
A novel approach is presented for the analysis and design of identification algorithms for Hammerstein models, which consist of a static nonlinearity followed by an LTI system. The authors examine two identification problems. In the first problem, the system is excited with white noise and the LTI system is FIR, and they find a simple explicit solution for the optimal parameter estimate and show that for sufficiently large data lengths a standard iterative technique globally converges to this optimal value. In the second problem, the LTI system is given in state-space form and the authors show that standard state-space algorithms can be easily modified to identify Hammerstein models.