TY - JOUR
T1 - Binary-Valued Identification of Nonlinear Wiener-Hammerstein Systems Using Adaptive Scheme
AU - Li, Linwei
AU - Zhang, Jie
AU - Wang, Fengxian
AU - Zhang, Huanlong
AU - Ren, Xuemei
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2023
Y1 - 2023
N2 - In the field of instrumentation and measurement science, quantized system identification based on sophisticated sensors has greatly reduced the cost of regular sensors. Although existing identification techniques are available, an identification algorithm with a novel framework and high estimation performance is required for new applications. This report is concerned with the system identification of a nonlinear Wiener-Hammerstein system with binary-valued measurements. In quantized system identification communities, stochastic approximation type scheme is a main direction of research by directly constructing an effective identification algorithm based on the error learning feedback principle. To overcome the difficulty in constructing the estimator by using the data directly related to parameter estimation (e.g., estimation error information, initial error information), this report aims to introduce a method to utilize the estimation error information, and to establish an adaptive estimator by combining the parameter initial error information. A novel-structured adaptive filter is introduced to improve the estimation bias phenomenon. By the use of auxiliary vectors and matrices, an estimation error representation is established. Then, the estimation error data with the conversion operator and initial error data with a smoothing factor are merged to derive the identifier, in which the time-varying gain is also provided. Theoretical analysis shows that the estimate reaches the true value of the parameter in the sense of almost surely. Numerical results and practical applications are supplied to clarify and verify the theoretical findings.
AB - In the field of instrumentation and measurement science, quantized system identification based on sophisticated sensors has greatly reduced the cost of regular sensors. Although existing identification techniques are available, an identification algorithm with a novel framework and high estimation performance is required for new applications. This report is concerned with the system identification of a nonlinear Wiener-Hammerstein system with binary-valued measurements. In quantized system identification communities, stochastic approximation type scheme is a main direction of research by directly constructing an effective identification algorithm based on the error learning feedback principle. To overcome the difficulty in constructing the estimator by using the data directly related to parameter estimation (e.g., estimation error information, initial error information), this report aims to introduce a method to utilize the estimation error information, and to establish an adaptive estimator by combining the parameter initial error information. A novel-structured adaptive filter is introduced to improve the estimation bias phenomenon. By the use of auxiliary vectors and matrices, an estimation error representation is established. Then, the estimation error data with the conversion operator and initial error data with a smoothing factor are merged to derive the identifier, in which the time-varying gain is also provided. Theoretical analysis shows that the estimate reaches the true value of the parameter in the sense of almost surely. Numerical results and practical applications are supplied to clarify and verify the theoretical findings.
KW - Error learning feedback
KW - parameter error
KW - quantized nonlinear system identification
KW - wiener Hammerstein system
UR - http://www.scopus.com/inward/record.url?scp=85168714630&partnerID=8YFLogxK
U2 - 10.1109/TIM.2023.3307760
DO - 10.1109/TIM.2023.3307760
M3 - Article
AN - SCOPUS:85168714630
SN - 0018-9456
VL - 72
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 3001110
ER -