Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

Abstract

In this study, a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented. The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks. Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process. The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals, thus the interference of process disturbance is solved. Furthermore, the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals, and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model. Finally, convergence analysis of the suggested identification scheme is derived using stochastic process theory. The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.