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《Nonlinear Timevarying System Identification Based on NARMA Model with Improved Recursive Least Square Scheme》
PENG Hai-bo;YU Kai-ping;LIU Wei
2010, 30 (2):
19-22.
DOI: 10.3969/j.issn.1006-1355.2010.02.019
Using the timevarying NARMA (Nonlinear Auto Regressive Moving Average) model and the improved recursive least square algorithm, an identification method for nonlinear timevarying structure system is proposed. Firstly, the dynamic model of the timeindependent structure system is changed to an autoregressivemovingaverage model by means of linear transform method. Then the nonlinear function of this model is expanded to a polynomial about input and output using Taylor expansion, and the polynomial timevarying NARMA model, which is a linear combination of parameters, is obtained. Using the basic sequences to fit the timevarying parameters of the model, the nonlinear timevarying system is then transformed into a linear timeinvariant system, whose parameters can be estimated by improved recursive least square algorithm. Finally, the proposed method is validated by the simulation of a 3DOF structural system with nonlinear timevarying stiffness.
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