变遗忘因子递推最小二乘Hammerstein系统辨识算法

本论文研究了单输入单输出非线性Hammerstein系统的辨识问题，提出了一种具有变遗忘因子的递推最小二乘算法。由于Hammerstein系统模型的非线性特征，传统的递推最小二乘算法无法直接用来解决该系统的辨识问题。为此，论文将Hammerstein系统参数进行了映射变换，使得变换后的系统参数与Hammerstein系统的输入输出构成一个线性关系，从而使系统能够适用于递推最小二乘算法；另外，为解决由于参数映射带来的收敛速度下降问题，论文进一步提出了一种变遗忘因子的递推最小二乘算法。仿真结果表明，提出的算法能够获得较好的收敛性和稳定性，并具有对非线性Hammerstein系统较高的辨识精度。 

Recursive Least Squares Algorithm with Variable Forgetting Factor for Identification in Hammerstein System

The paper studies the identification of single-input single-output nonlinear Hammerstein systems， and proposes a variable forgetting factor recursive least squares algorithm. Due to the nonlinear characteristics of the Hammerstein system model， the traditional recursive least squares algorithm cannot be directly used to solve the identification problem of the system. In order to solve this problem， the Hammerstein system parameters are mapped and transformed so that the transformed system parameters and the input and output of the Hammerstein system form a linear relationship. In this way， the system can be applied to the recursive least squares algorithm； Moreover， in order to solve the problem of the decrease in convergence speed caused by parameter mapping， the paper further proposes a recursive least squares algorithm with variable forgetting factor. The simulation results show that the proposed algorithm can obtain better convergence and stability， and improve the system identification accuracy.