This paper considers the parameter identification of neuro-fuzzy based Hammerstein output error auto-regressive (OEAR) systems by combining multiple signal source separation principle and auxiliary model identification idea. The unmeasurable internal variable is replaced by the correlation function of input and output data, then correlation analysis method is adopted to identify the parameters of linear part. In order to solve the parameter identification of the nonlinear part and the noise model, this paper presents a recursive generalized least squares algorithm based on auxiliary model. The convergence analysis in stochastic process theory shows that the parameter estimation error converges to zero under the persistent excitation condition. Examples results indicate that the proposed algorithm has significant advantages of good recognition accuracy to noise disturbance.

结合多信号源分离原理和辅助模型辨识思想，研究基于神经模糊的Hammerstein输出误差自回归（OEAR）系统的参数辨识。不可测的内部变量由输入和输出数据的相关函数代替，然后采用相关分析方法识别线性零件的参数。为了解决非线性部分和噪声模型的参数辨识问题，提出了一种基于辅助模型的递归广义最小二乘算法。随机过程理论的收敛性分析表明，在持续激励条件下，参数估计误差收敛为零。