Hierarchical Newton Iterative Parameter Estimation of a Class of Input Nonlinear Systems Based on the Key Term Separation Principle

This paper investigates the identification problem for a class of input nonlinear systems whose disturbance is in the form of the moving average model. In order to improve the computation complexity, the key term separation principle is introduced to avoid the redundant parameter estimation. Based on the decomposition technique, a hierarchical Newton iterative identification method combining the key term separation principle is proposed for enhancing the estimation accuracy and handling the computational load with the presence of the high dimensional matrices. In the identification procedure, the unknown internal items or vectors are replaced with their iterative estimates. The effectiveness of the proposed identification methods is shown via a numerical simulation example.

本文研究了一类干扰为移动平均模型形式的输入非线性系统的辨识问题。为了改善计算复杂度，引入关键项分离原理以避免冗余参数估计。基于分解技术，提出了一种结合关键项分离原理的分层牛顿迭代辨识方法，以提高估计精度并在存在高维矩阵的情况下处理计算负荷。在辨识过程中，未知的内部项或向量用其迭代估计值代替。通过一个数值仿真实例展示了所提出的辨识方法的有效性。