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• W4313159972 abstract "In this paper, parameter identification method of nonlinear systems as Hammerstein model based on auxiliary model technique is proposed. The developed Hammerstein model is composed of a static nonlinear block approximated by neural fuzzy model and a dynamical linear block modeled by transfer function, and parameters identification separation of the Hammerstein model are realized by using combined signals composed of binary signal and random signal. Firstly, by virtue of the characteristics that the binary signals do not excite the static nonlinear system, the linear block parameters are identified according to auxiliary model-based least square method. Moreover, to improve the identification accuracy, the multi-innovation theory is used, and auxiliary model technique is adopted to solve immeasurable variables in identified system, thus multi-innovation stochastic gradient approach based on auxiliary model is derived for identifying the static nonlinear block parameters. Experimental results show that the proposed two-stage parameter identification method can effectively estimate nonlinear systems as Hammerstein model." @default.
• W4313159972 created "2023-01-06" @default.
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• W4313159972 date "2022-07-25" @default.
• W4313159972 modified "2023-10-15" @default.
• W4313159972 title "Identification of Nonlinear Systems as Hammerstein model Using Auxiliary Model Technique" @default.
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• W4313159972 doi "https://doi.org/10.23919/ccc55666.2022.9902291" @default.
• W4313159972 hasPublicationYear "2022" @default.
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