基于组合式信号源的非线性系统辨识

针对非线性系统中噪声的干扰，研究了一类神经模糊Hammerstein输出误差非线性系统的建模和辨识方法，利用组合式信号源实现静态非线性模块和动态线性模块参数辨识的分离，推导了相关性分析法和辅助模型递推最小二乘辨识方法估计动态线性模块和非线性模块的参数，有效抑制系统输出噪声的干扰。仿真结果表明：与最小二乘算法、多项式模型以及多信息方法相比，提出的方法具有参数估计收敛速度快，辨识精度高，建模误差小等优势，验证了所提学习算法的有效性。

Nonlinear System Identification Based on Combined Signal Sources

Aiming at the interference of noise in the nonlinear system, the identification modeling method of the neuro-fuzzy Hammerstein output error nonlinear system is considered. The combined signal sources are used to realize the parameter identification separation of the linear block and the nonlinear block. The correlation analysis method and the recursive least square identification method based on auxiliary model technique are derived to estimate the parameters of dynamic linear block and nonlinear block, which can effectively suppress the interference of system output noise. Compared with least square algorithm, polynomial model and multi-innovation method, the simulation results demonstrate that the proposed approach has the advantages of fast convergence speed of parameter estimation, high identification accuracy and small modeling error, which verifies the effectiveness of the proposed approach.