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.
Parameter Estimation Algorithms for Hammerstein–Wiener Systems With Autoregressive Moving Average Noise
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.
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Wang, Y., and Ding, F. (October 23, 2015). "Parameter Estimation Algorithms for Hammerstein–Wiener Systems With Autoregressive Moving Average Noise." ASME. J. Comput. Nonlinear Dynam. May 2016; 11(3): 031012. https://doi.org/10.1115/1.4031420
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