Auxiliary Model Based Multi-innovation Stochastic Gradient Identification Methods for Hammerstein Output-Error System  

作　　者：冯启亮 贾立 李峰 

机构地区：[1]Shanghai Key Laboratory of Power Station Automation Technology, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China

出　　处：《Journal of Donghua University(English Edition)》2017年第1期53-59,共7页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.61374044);Shanghai Science Technology Commission,China(Nos.15510722100,16111106300)

摘　　要：Special input signals identification method based on the auxiliary model based multi-innovation stochastic gradient algorithm for Hammerstein output-error system was proposed.The special input signals were used to realize the identification and separation of the Hammerstein model.As a result,the identification of the dynamic linear part can be separated from the static nonlinear elements without any redundant adjustable parameters.The auxiliary model based multi-innovation stochastic gradient algorithm was applied to identifying the serial link parameters of the Hammerstein model.The auxiliary model based multi-innovation stochastic gradient algorithm can avoid the influence of noise and improve the identification accuracy by changing the innovation length.The simulation results show the efficiency of the proposed method.Special input signals identification method based on the amdh＇ary model based multi-innovation stochastic gradient algorithm for Hammerstein output-error system was proposed. The special input signals were used to realize the identification and separation of the Hammerstein model. As a result, the identification of the dynamic linear part can be separated from the static nonlinear dements without any redundant adjustable parmeters. The auxiliary model based multi-innovation stochastic gradient algorithm was applied to identifying the serial link parameters of the Hammerstein model. The auxiliary model based multi-innovation stochastic gradient algorithm can avoid the influence of noise and improve the identification accuracy by charting the innovation length. The simulation results show the efficiency of the proposed method.

关 键 词：Hammerstein output-error system special input signals auxiliary model based multi-innovation stochastic gradient algorithm innovation length 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]