This article investigates the parameter estimation problems of Hammerstein-Wiener nonlinear systems with non-uniform sampling. The over-parameterization identification model for the Hammerstein-Wiener nonlinear systems is established from the non-uniformly sampled input-output data. By applying the gradient search principle, we derive an over-parameterization forgetting factor stochastic gradient algorithm for identifying the nonlinear systems. In order to improve the parameter estimation accuracy, a decomposition-based over-parameterization forgetting factor stochastic gradient algorithm is presented by using the decomposition technique. The key is to transform the original system into two subsystems and to estimate the parameters of each subsystem, respectively. The simulation results indicate that the proposed algorithms are effective.

中文翻译：

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非均匀采样Hammerstein-Wiener非线性系统的基于分解的过参数化遗忘因子随机梯度算法

本文研究了具有非均匀采样的 Hammerstein-Wiener 非线性系统的参数估计问题。Hammerstein-Wiener非线性系统的过参数化识别模型是从非均匀采样的输入输出数据中建立的。通过应用梯度搜索原理，我们推导出了一种用于识别非线性系统的过参数化遗忘因子随机梯度算法。为了提高参数估计精度，利用分解技术提出了一种基于分解的过参数化遗忘因子随机梯度算法。关键是将原系统转化为两个子系统，分别估计每个子系统的参数。仿真结果表明所提出的算法是有效的。