融合改进蚁狮算法和T-S模糊模型的噪声非线性系统辨识

Identification of nonlinear system with noise based on improved ant lion optimization and T-S fuzzy model

针对传统的T-S模糊辨识方法难以准确辨识含噪声的非线性系统问题,将噪声信号和系统的其他输入变量一起作为模糊前件的输入,采用具有动态随机搜索和寻优半径连续收缩机制的改进蚁狮算法优化模糊前件的结构参数,使用加权最小二乘法实现模糊后件的参数辨识.数值仿真表明,所提出的辨识方法可以有效抑制噪声的影响,经过改进蚁狮算法优化后的T-S模糊模型辨识效果更好.最后,将所提出方法用于直拉硅单晶生长热模型的辨识,实验结果表明该方法优于传统的辨识方法.

For the identification of nonlinear systems with noise, the traditional T-S fuzzy identification method is difficult to get better results. Therefore the noise signal is regarded as the input of the antecedent together with other input variables of the system. The improved ant lion optimization(ALO) algorithm with dynamic random search and continuous radius contraction is used to optimize the structural parameters of the antecedent. The weighted least square method is utilized to identify the parameters in the consequent. The simulation results show that the proposed method can effectively repress the noise, and achieve better identification effect by using the improved ALO algorithm. Finally, the proposed method is applied to the identification of the thermal model of CZ silicon single crystal growth, and the experimental results show that it is superior to the traditional identification method.