基于组合式信号源的Hammerstein-Wiener模型辨识方法

针对含有有色噪声的非线性Hammerstein-Wiener模型,提出一种基于组合式信号源的辨识方法.通过利用可分离信号和随机信号组成的组合信号源实现有色噪声干扰下Hammerstein-Wiener模型各串联模块参数辨识的分离,简化辨识过程.首先,基于可分离信号的输入和输出,采用相关分析方法抑制过程噪声的干扰,辨识输出静态非线性模块和动态线性模块的参数;然后,基于辅助模型技术,利用辅助模型的输出和残差的估计值分别取代辨识模型中的不可测中间变量和噪声变量,推导辅助模型递推增广最小二乘方法,根据随机信号的输入输出数据辨识输入静态非线性模块和噪声模型的参数;最后,通过理论分析和仿真结果表明,所提出方法能够有效辨识有色噪声干扰下的非线性Hammerstein-Wiener模型,具有较好的鲁棒性.

Identification method of the Hammerstein-Wiener model based on combined signal sources

An identification method based on combined signal sources is proposed to identify the nonlinear Hammerstein-Wiener model with coloured noise. The combined signal sources composed of separable signals and random signals are used to realize the separation of the parameter identification of the series modules for the Hammerstein-Wiener model with coloured noise, which can effectively simplifies the identification process. Firstly, based on the input and output of separable signals, the correlation analysis method is employed to compensate the process noise and identify the parameters of the static nonlinear module and the dynamic linear module. Then, by means of the auxiliary model technique, the output of the auxiliary model and residual estimation are used to replace the unmeasurable intermediate variables and noise variables in the identification model, respectively. The recursive extended least square method based on the auxiliary model is deduced to identify the parameters of the input static nonlinear module and noise model according to the input and output data of random signals. Theoretical analysis and simulation results show that the proposed method can effectively identify the nonlinear Hammerstein-Wiener model with coloured noise and has good robustness.