Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance

• Published in: Measurement and control (London) Vol. 53; no. 5-6; pp. 971 - 982
• Main Authors: Li, Feng, Chen, Lianyu, Wo, Songlin, Li, Shengquan, Cao, Qingfeng
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.05.2020; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Artificial neural networks; Complexity; Computer simulation; Correlation analysis; Fuzzy logic; Impulse response; Learning; Least squares method; Mathematical models; Parameters; Random signals; Robustness (mathematics); Signal processing; Teaching methods; Hammerstein–Wiener model; process disturbance; designed input signals; parameter learning
• ISSN: 0020-2940; 2051-8730
• DOI: 10.1177/0020294020912790

In this paper, a novel modeling and parameter learning method for the Hammerstein–Wiener model with disturbance is proposed, and the Hammerstein–Wiener model is implemented to approximate complex nonlinear industrial processes. The proposed Hammerstein–Wiener model has two static nonlinear blocks represented by two independent neuro-fuzzy models that surround a dynamic linear block described by the finite impulse response model. The parameter learning method of the Hammerstein–Wiener model with disturbance can be summarized in the following three steps: First, the designed input signals are implemented to completely separate the parameter learning problem of output nonlinear block, linear block, and input nonlinear block. Meanwhile, the static output nonlinear block parameters can be learned based on input and output data of two sets of separable signals with different sizes. Second is to determine the dynamic linear block parameter using correlation analysis algorithm using one set of separable signal; thus, the process disturbance can be compensated by the calculation of correlation function. The final one is to achieve unbiased estimation of the static input nonlinear block parameters using least squares method according to the input–output data of random signal. Furthermore, with the parameter learning method, the proposed model can achieve less computation complexity and good robustness. The simulation results of two cases are provided to demonstrate the advantage of the proposed modeling and parameter learning method.