基于移动数据窗的传递函数多新息随机梯度辨识方法  

Moving data window based multi-innovation identification stochastic gradient parameter identification method for transfer functions

作　　者：徐玲[1,2] XU Ling(School of the Internet of Things Engineering, Jiangnan University, Wuxi 214122, China School of the Internet of Things Technology, Wuxi Vocational Institute of Commerce, Wuxi 214153, China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122 [2]无锡商业职业技术学院物联网技术学院,江苏无锡214153

出　　处：《控制与决策》2017年第6期1091-1096,共6页Control and Decision

基　　金：国家自然科学基金项目(61273194);江苏省高校自然科学基金项目(16KJB120007);江苏省"青蓝工程"资助项目

摘　　要：一些工业过程可以近似用一个传递函数描述,结合统计辨识方法和非线性优化策略提出传递函数参数辨识方法.该方法采用动态数据方案,使用系统观测数据获得系统更多的模态信息.基于动态观测数据,提出传递函数随机梯度参数辨识方法.为进一步提高辨识精度,利用动态窗数据将随机梯度参数辨识方法中的标量新息扩展为新息向量,提出传递函数多新息随机梯度参数估计方法.最后通过仿真例子对所提出的方法进行了性能分析和模型验证.For industrial process transfer functions, this paper proposes a parameter estimation method combining the statistical identification method and the nonlinear optimization strategy. In order to obtain enough system modal information, a moving data window is used to deduce the identification method. Based on the dynamical measured data, a stochastic gradient parameter estimation algorithm is presented. In order to improve the estimation accuracy, a multi-innovation stochastic gradient parameter identification method is proposed for transfer functions by expanding the innovation scalar into the innovation vector. Finally, a simulation example is given to compare the proposed methods. Meanwhile the performance of the proposed algorithms is analyzed and the estimated models are verified by step response tests.

关 键 词：系统辨识 参数估计 随机梯度 多新息 传递函数 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]