| 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 |
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| 引用本文： | 赵小国,刘丁,景坤雷.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[J].控制与决策,2019,34(4):759-766. |
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| 作者姓名： | 赵小国 刘丁 景坤雷 |
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| 作者单位： | 西安理工大学 晶体生长设备及系统集成国家地方联合工程研究中心,西安 710048;西安理工大学 陕西省复杂系统控制与智能信息处理重点实验室,西安 710048;西安建筑科技大学 机电工程学院,西安 710055;西安理工大学 晶体生长设备及系统集成国家地方联合工程研究中心,西安 710048;西安理工大学 陕西省复杂系统控制与智能信息处理重点实验室,西安 710048 |
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| 基金项目： | 国家自然科学基金重点项目(61533014)；国家科技重大专项基金项目(2009ZX02011001)；陕西省教育厅专项科研计划项目(17JK0456). |
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| 摘 要： | 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.
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| 关 键 词： | 蚁狮算法 T-S模糊模型 噪声 非线性系统 直拉硅单晶 |
Identification of nonlinear system with noise based on improved ant lion optimization and T-S fuzzy model |
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| ZHAO Xiao-guo,LIU Ding and JING Kun-lei.Identification of nonlinear system with noise based on improved ant lion optimization and T-S fuzzy model[J].Control and Decision,2019,34(4):759-766. |
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| Authors: | ZHAO Xiao-guo LIU Ding and JING Kun-lei |
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| Affiliation: | National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration,Xián University of Technology, Xián 710048,China;Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xián University of Technology,Xián710048,China;School of Mechanical and Electrical Engineering,Xián University of Architecture and Technology,Xián710055,China,National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration,Xián University of Technology, Xián 710048,China;Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xián University of Technology,Xián710048,China and National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration,Xián University of Technology, Xián 710048,China;Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xián University of Technology,Xián710048,China |
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| Abstract: | 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. |
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