Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Abstract

Abstract In this paper, we present a methodology for identifying the multi-input multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented is comprised of neural fuzzy models (NFM) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, i.e., the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function based least squares method in the presence of measurable input-output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is to introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input-output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a practical nonlinear process, and it is known from the research results, that compare with existing identification techniques, the methodology utilized achieved higher identification accuracy.