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.

Recently, some novel optimization algorithms, such as populationbased optimization method [12], dwarf mongoose optimization algorithm [13], Ebola optimization search algorithm [14], and reptile search algorithm [15], have been presented to handle successfully system design or engineering design, which would inspire researchers to take interest. Also, these optimization methods can be used for Hammerstein system identification.Problem statements: Neural network and fuzzy system have been applied widely to nonlinear system modeling since that they show strong nonlinear approximation ability in recent years. It should be noted that neural networks have strong ability of the self-learning, but it is lack of reasoning ability of human brain. On the contrary, the

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This article develops a novel separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis technique. The Hammerstein‐Wiener nonlinear systems have three parts, namely, an input nonlinear block, a linear block, and an output nonlinear block. The designed hybrid signals that consist of separable signal and random signal are devoted to achieving parameters separation identification of the Hammerstein‐Wiener nonlinear system, that is, the three blocks are identified independently. First, the characteristics of separable signals under the action of static nonlinear block are analyzed, and two groups of separable signals with amplitude relation are utilized to estimate parameters of output nonlinear block. Moreover, the linear block parameters are identified by using correlation analysis approach, which deals with effectively immeasurable problem of internal variable information. Finally, the data filtering technique is implemented to weaken the influence of noises, and filtering‐based recursive extended least squares algorithm is developed for figuring out the parameters of nonlinear block and colored noise model. The validity and accuracy of the proposed scheme are verified by two simulations, and simulation results exhibit that the proposed method can obtain higher identification precision and better robustness than the existing identification algorithms.

This study investigates a two-stage parameter identification algorithm for the Hammerstein nonlinear system based on special test signals. The studied Hammerstein nonlinear system has a static nonlinear subsystem represented by polynomial basis function and a dynamic linear subsystem described by canonical observable state space model, and special test signals composed of binary signals and random signals are applied to parameter identification separation of the nonlinear subsystem and linear subsystem. The detailed identification procedures consist of two main steps. Firstly, using the characteristics that binary signals do not excite the static nonlinear subsystem, the dynamic linear subsystem parameters are identified through recursive least squares algorithm based on input-output data of binary signals. Secondly, unmeasurable state variables of the identified system are replaced with estimated values, thus the nonlinear subsystem parameters are obtained using recursive least squares algorithm with the help of input-output data of random signals. The efficiency and accuracy of proposed identification scheme are confirmed on experiment results of a numerical simulation and a practical nonlinear process, and experimental simulation results show that the developed two-stage identification algorithm has excellent predictive performance for identifying the Hammerstein nonlinear state space systems.