Hammerstein系统遗忘因子有限窗口分解辨识

提出一种带遗忘因子和分解辨识策略的有限数据窗口递归最小二乘Hammerstein系统辨识方法。针对Hammerstein系统具有耦合参数的问题，将Hammerstein系统分解为2个子系统：一个子系统包含线性子系统参数，另一个子系统包含非线性子系统参数；提出一种基于遗忘因子的有限窗口递归最小二乘方法对分解模型进行在线递归估计；仿真示例验证了所提算法能够快速跟踪参数，实现对Hammerstein系统的精确辨识。

Identification of Hammerstein systems using decomposition based finite-data-window recursive least squares method with a forgetting factor

In this paper, a decomposition based recursive finite-data-window least squares identification method with a forgetting factor is proposed for Hammerstein systems. The proposed method aims to identify the parameters of Hammerstein systems by decomposing them into two subsystems, one involving linear subsystem parameters, and the other containing the nonlinear subsystem parameters. To achieve this, a two-step finite-data-window recursive least squares method with a forgetting factor is developed. To verify the effectiveness and merits of the proposed algorithm, a simulation example is provided, demonstrating that the proposed algorithm can quickly track parameters and accurately and effectively identify Hammerstein systems.