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Research Papers

Parameter Estimation Algorithms for Hammerstein–Wiener Systems With Autoregressive Moving Average Noise

[+] Author and Article Information
Yanjiao Wang

Key Laboratory of Advanced Process Control for
Light Industry (Ministry of Education),
Jiangnan University,
Wuxi 214122, China
e-mail: yjwang12@126.com

Feng Ding

Key Laboratory of Advanced Process Control for
Light Industry (Ministry of Education),
Jiangnan University,
Wuxi 214122, China
e-mail: fding@jiangnan.edu.cn

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received April 12, 2015; final manuscript received August 19, 2015; published online October 23, 2015. Assoc. Editor: Sotirios Natsiavas.

J. Comput. Nonlinear Dynam 11(3), 031012 (Oct 23, 2015) (8 pages) Paper No: CND-15-1096; doi: 10.1115/1.4031420 History: Received April 12, 2015; Revised August 19, 2015

Hammerstein–Wiener (H–W) systems are a class of typical nonlinear systems. This paper studies the gradient-based parameter estimation algorithms for H–W nonlinear systems based on the multi-innovation identification theory and the data filtering technique. The proposed methods include a generalized extended stochastic gradient (GESG) algorithm, a multi-innovation GESG (MI-GESG) algorithm, a data filtering based GESG (F-GESG) algorithm and a data filtering based MI-GESG algorithm. Finally, the computational efficiency of the proposed algorithms are analyzed and compared. The simulation example verifies the theoretical results.

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References

Figures

Grahic Jump Location
Fig. 1

The GESG and MI-GSEG estimation errors versus t

Grahic Jump Location
Fig. 2

The F-GESG and F-MI-GESG estimation errors versus t

Grahic Jump Location
Fig. 3

The predicted outputs and the true outputs from t = 8001 to t = 9000

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