Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein-Wiener nonlinear systems based on non-uniform sampling. By means of the auxiliary model identification idea, an auxiliary model-based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model-based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.

中文翻译：

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基于非均匀采样的一类非线性系统的递推最小二乘估计方法

实践中的许多动态过程都具有非线性特征，必须使用非线性模型来描述。建立这种非线性系统的模型并估计它们的参数仍然是一个具有挑战性的问题。本文研究了一类基于非均匀采样的Hammerstein-Wiener非线性系统的参数估计问题。借助辅助模型辨识思想，推导出了一种基于辅助模型的递推最小二乘算法。为了提高计算效率，利用层次识别原理，提出了一种基于辅助模型的层次最小二乘算法。仿真结果证实了所提出算法的有效性。