文献类型：期刊文献

英文题名：The Filtering Based Maximum Likelihood Recursive Least Squares Parameter Estimation Algorithms for a Class of Nonlinear Stochastic Systems with Colored Noise

作者：Wang, Longjin[1];An, Shun[1];He, Yan[1];Yuan, Jianping[2]

机构：[1]Qingdao Univ Sci & Technol, Coll Mech & Elect Engn, Qingdao 266061, Peoples R China;[2]Guangdong Ocean Univ, Coll Ocean Engn, Zhanjiang 524088, Guangdong, Peoples R China

年份：2023

卷号：21

期号：1

起止页码：151

外文期刊名：INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:000901704100002)、、EI(收录号：20225213293289)、Scopus(收录号：2-s2.0-85144455477)、WOS

基金：This work was supported by the Taishan Scholar Project of Shandong Province (ts20190937), National Natural Science Foundation of China (52176076, 52101401), Guangdong Province in 2019 Ordinary University Key Areas Special Project (2019KZDZX1024) and State Administration of Science, Technology and Industry for National Defense (JCKYS2021SXJQR-02).

语种：英文

外文关键词：Bilinear system; data filtering; least squares; maximum likelihood

外文摘要：This paper focuses on the maximum likelihood estimation for bilinear systems in the presence of colored noise. The state variables in the model is eliminated and an input-output expression is provided. The input-output data of the system is filtered by an estimated noise transfer function, and the system is transformed into two subsystems. A filtering based maximum likelihood recursive least squares algorithm is proposed to strengthen the identification accuracy and improve computational efficiency. The superior performance of the developed methods are demonstrated by numerical simulations.