Regular Papers

International Journal of Control, Automation and Systems 2021; 19(8): 2706-2715

Published online June 16, 2021

https://doi.org/10.1007/s12555-019-1060-y

© The International Journal of Control, Automation, and Systems

Two-stage Gradient-based Recursive Estimation for Nonlinear Models by Using the Data Filtering

Yan Ji*, Zhen Kang, and Chen Zhang

Qingdao University of Science and Technology

Abstract

This paper considers the parameter estimation problem of a two-input single-output Hammerstein finite impulse response system with autoregressive moving average noise. Applying the data filtering technique, the inputoutput data is filtered and the original system with autoregressive moving average noise is changed into the system with moving average noise. Then, based on the key term separation technique, the filtered system is decomposed into two subsystems: one subsystem contains the unknown parameters in the nonlinear block, the other contains the unknown parameters in the linear dynamic block and the noise model. A filtering based multi-innovation stochastic gradient algorithm is presented for Hammerstein finite impulse response systems. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of two-input single-output Hammerstein finite impulse response systems.

Keywords Filtering technique, gradient search, Hammerstein system, key term separation, parameter estimation

Article

Regular Papers

International Journal of Control, Automation and Systems 2021; 19(8): 2706-2715

Published online August 1, 2021 https://doi.org/10.1007/s12555-019-1060-y

Copyright © The International Journal of Control, Automation, and Systems.

Two-stage Gradient-based Recursive Estimation for Nonlinear Models by Using the Data Filtering

Yan Ji*, Zhen Kang, and Chen Zhang

Qingdao University of Science and Technology

Abstract

This paper considers the parameter estimation problem of a two-input single-output Hammerstein finite impulse response system with autoregressive moving average noise. Applying the data filtering technique, the inputoutput data is filtered and the original system with autoregressive moving average noise is changed into the system with moving average noise. Then, based on the key term separation technique, the filtered system is decomposed into two subsystems: one subsystem contains the unknown parameters in the nonlinear block, the other contains the unknown parameters in the linear dynamic block and the noise model. A filtering based multi-innovation stochastic gradient algorithm is presented for Hammerstein finite impulse response systems. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of two-input single-output Hammerstein finite impulse response systems.

Keywords: Filtering technique, gradient search, Hammerstein system, key term separation, parameter estimation

IJCAS
February 2024

Vol. 22, No. 2, pp. 347~729

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