TY - GEN
T1 - Robust Estimation for Hammerstein Models Based on Variational Inference
AU - Ma, Zhengya
AU - Wang, Xiaoxu
AU - Li, Rui
AU - Cui, Haoran
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The paper presents a robust identification method using variational inference (VI) for Hammerstein models in the presence of process noise and non-Gaussian colored measurement noise. First of all the measurements and process output are described as Student's t and Gaussian distribution by using introduced variational parameters. Then the conjugate prior information of introduced parameters is framed for sake of a closed-loop solution. By applying the idea of VI, estimates of system parameters are got by minimizing Kullback-Leibler (KL) divergence. Finally, a numerical simulation example is used to show the effectiveness of the proposed identification method compared with the traditional method.
AB - The paper presents a robust identification method using variational inference (VI) for Hammerstein models in the presence of process noise and non-Gaussian colored measurement noise. First of all the measurements and process output are described as Student's t and Gaussian distribution by using introduced variational parameters. Then the conjugate prior information of introduced parameters is framed for sake of a closed-loop solution. By applying the idea of VI, estimates of system parameters are got by minimizing Kullback-Leibler (KL) divergence. Finally, a numerical simulation example is used to show the effectiveness of the proposed identification method compared with the traditional method.
KW - robust estimation
KW - system identification
KW - variational inference
UR - http://www.scopus.com/inward/record.url?scp=85151137660&partnerID=8YFLogxK
U2 - 10.1109/CAC57257.2022.10055938
DO - 10.1109/CAC57257.2022.10055938
M3 - 会议稿件
AN - SCOPUS:85151137660
T3 - Proceedings - 2022 Chinese Automation Congress, CAC 2022
SP - 2716
EP - 2721
BT - Proceedings - 2022 Chinese Automation Congress, CAC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Chinese Automation Congress, CAC 2022
Y2 - 25 November 2022 through 27 November 2022
ER -