基于特殊历史数据段挖掘的切换Hammerstein模型辨识  

Identification of switching Hammerstein model based on mining special historical data segments

作　　者：贾晓彤 王建东 邢晓彤 Jia Xiaotong;Wang Jiandong;Xing Xiaotong(College of Electrical and Automation Engineering,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]山东科技大学电气与自动化工程学院,青岛266590

出　　处：《电子测量技术》2023年第9期85-91,共7页Electronic Measurement Technology

基　　金：国家自然科学基金面上项目(62273215)资助。

摘　　要：本文提出了一种基于特殊历史数据段挖掘的切换Hammerstein模型辨识方法。特殊数据段是指处于稳定状态和稳定斜坡响应的数据段。首先,根据稳态数据采用随机抽样一致算法辨识静态非线性子系统。其次,根据稳定的斜坡响应数据利用密度峰值聚类算法辨识切换Hammerstein模型线性动态子系统的结构和相应的操作区间。最后,根据操作区间划分历史数据集,采用最小二乘算法辨识切换的多个线性动态子系统的模型参数。数值仿真和实验案例结果表明,与标准Hammerstein辨识方法相比,所提方法可以实现不同工作点切换的多个线性动态子模型的结构辨识及操作区间划分,降低了模型结构未知时切换动态子系统对模型参数辨识的影响,提高了切换Hammerstein模型的辨识精度。This paper proposes a switched Hammerstein model identification method based on special historical data segment mining.Special data segment refers to data in stable state and stable slope response.First,a random sampling consensus algorithm is used to identify the static nonlinear subsystem based on the steady-state data.Secondly,based on the stable slope response data,the density peak clustering algorithm is used to identify the dynamic subsystem structure and the corresponding operation interval.Finally,the least squares algorithm is used to identify the model parameters of the switched dynamic subsystems based on the data sets in the operation interval.The results of numerical simulation and experimental cases show that,compared with the standard Hammerstein identification method,the proposed method can realize the structure identification and operation interval division of multiple linear dynamic submodules with different switching points,reduce the influence of switching dynamic subsystems on parameter identification when the model structure is unknown,and improve the identification accuracy of switching Hammerstein models.

关 键 词：Hammerstein系统辨识 动态子系统结构辨识 参数估计 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]