Title :
Parametric Hammerstein-Wiener model estimation via dual Hammerstein identification
Author :
Jianrui Long ; Williamson, Geoffrey A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
Abstract :
In this paper, we propose an approach to identify parametric Hammerstein-Wiener models. The approach identifies two Hammerstein models alternately, recovering the intermediate signal and parameters in both linear dynamic blocks and static nonlinear blocks. Identification of Hammerstein models can be implemented by using the iterative method or the two-stage over-parametrization method, both leading to efficient computations. Simulation results show that our approach converges fast, and is robust when the input or output blocks are highly nonlinear.
Keywords :
iterative methods; parameter estimation; signal processing; dual Hammerstein identification; intermediate signal recovery; iterative method; linear dynamic blocks; parametric Hammerstein-Wiener model estimation; static nonlinear blocks; two-stage over-parametrization method; Algorithm design and analysis; Computational modeling; Convergence; Estimation; Heuristic algorithms; Optimization; Polynomials; Hammerstein-Wiener model; block-oriented system identification; parametrized model;
Conference_Titel :
Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 2013 IEEE
Conference_Location :
Napa, CA
Print_ISBN :
978-1-4799-1614-6
DOI :
10.1109/DSP-SPE.2013.6642565