Wiener model identification of highly nonlinear systems using Matlab
- A fast iterative recursive least squares algorithm is presented to identify the nonlinear system.
- This method is fast and needs a few data sets.
- The intermediate signal of the Wiener model is estimated by Least squares algorithm.
- In order to increase the robustness of the proposed method, a robust RLS algorithm is applied to the model.
- Simulation results confirm the effectiveness of the proposed approach.
Abstract (Base Paper):
In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output coloured noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has a fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.
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