Parameter Estimation of the Hammerstein Output Error Model Using Multi-signal Processing

Parameter identification for Hammerstein nonlinear system with polynomial and state space model

“…Previous research results show that the Hammerstein process is identified by using binary signals, 50 which disclosed that for the binary input signals in Figure 2, after passing through the nonlinear subsystem, the intermediate variable v ( k ) are binary signals with different amplitude of the same frequency as u ( k ) , as shown in Figure 2(a). Using the input approximation instead of the intermediate variable v ( k ) , the amplitude difference can be compensated with the constant gain factor β , as shown in Figure 2(b).…”

Data-Driven Hybrid Neural Fuzzy Network and ARX Modeling Approach to Practical Industrial Process Identification

“…From the perspective of research limitations, the colored noise considered is only linear combination driven by white noise, the correlation of noise at different times is not researched. Our future works will be carried out from two aspects: firstly, some novel optimization algorithms [12]− [15] can be used for identification of practical process represented by Hammerstein system. In addition, the nonlinear subsystem of the Hammerstein system is modeled by deep neural network models [22].…”

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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.

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