Non-parametric nonlinear system identification: An asymptotic minimum mean squared error estimator
Non-parametric nonlinear system identification: An asymptotic minimum mean squared error estimator
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DOI：
10.1109/cdc.2009.5400648
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发表时间：
2009-12
期刊：
影响因子：
--
通讯作者：
E. Bai
中科院分区：
文献类型：
--
作者：
E. Bai
本文研究非参数非线性系统辨识的最小均方误差估计问题。证明了对于广泛的一类非线性系统，局部线性估计是线性（输出）渐近最小均方误差估计。允许的系统类的特点是由一个稳定性条件，这是有关许多研究文献中的稳定性概念。数值模拟支持分析。
This paper studies the problem of the minimum mean squared error estimator for non-parametric nonlinear system identification. It is shown that for a wide class of nonlinear systems, the local linear estimator is a linear (in outputs) asymptotic minimum mean squared error estimator. The class of the systems allowed is characterized by a stability condition that is related to many well studied stability notions in the literature. Numerical simulations support the analytical analysis.