TY - GEN
T1 - Adaptive Estimation for Quantized Nonlinear Cascade System
AU - Li, Linwei
AU - Wang, Ying
AU - Wang, Fengxian
AU - Zhang, Jie
AU - Li, Linwei
AU - Ren, Xuemei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we introduce an adaptive estimation method for quantized nonlinear cascade system using moving window theory. Firstly, by force of the sub-decomposition technique, the considered system is transformed to a regression model without product term, in which the computational complexity is reduced. Secondly, by developing a moving window, the moving window output and moving window observation data are constructed, in which the estimation accuracy is lifted. Then, based on moving data, a filter is introduced to filter noise data, and to improve the bias estimation issue. Thirdly, by designing the forcing variables with adaptive attenuation coefficient, the estimation error data can be got which is used to develop estimator, in which it gives an optional scheme to design the adaptive estimator compared with the prediction error and observation error criterion. Finally, the example results demonstrate that the developed method is effective to achieve the parameter estimation for quantized nonlinear cascade system, and the has better performance compared with some estimators in term of estimation precision and convergence rate.
AB - In this paper, we introduce an adaptive estimation method for quantized nonlinear cascade system using moving window theory. Firstly, by force of the sub-decomposition technique, the considered system is transformed to a regression model without product term, in which the computational complexity is reduced. Secondly, by developing a moving window, the moving window output and moving window observation data are constructed, in which the estimation accuracy is lifted. Then, based on moving data, a filter is introduced to filter noise data, and to improve the bias estimation issue. Thirdly, by designing the forcing variables with adaptive attenuation coefficient, the estimation error data can be got which is used to develop estimator, in which it gives an optional scheme to design the adaptive estimator compared with the prediction error and observation error criterion. Finally, the example results demonstrate that the developed method is effective to achieve the parameter estimation for quantized nonlinear cascade system, and the has better performance compared with some estimators in term of estimation precision and convergence rate.
KW - Quantized nonlinear cascade system
KW - adaptive estimation
KW - filter design
KW - parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85137809507&partnerID=8YFLogxK
U2 - 10.1109/DDCLS55054.2022.9858456
DO - 10.1109/DDCLS55054.2022.9858456
M3 - Conference contribution
AN - SCOPUS:85137809507
T3 - Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022
SP - 630
EP - 635
BT - Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022
A2 - Sun, Mingxuan
A2 - Chen, Zengqiang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022
Y2 - 3 August 2022 through 5 August 2022
ER -