Learning-based frequency response function estimation for nonlinear systems

Suresh Thenozhi and Yu Tang

International Journal of Systems Science, 2018, vol. 49, issue 11, 2287-2297

Abstract: In this paper, we perform the nonlinear frequency response function (FRF) estimation for a class of nonlinear systems. Two non-parametric estimation techniques are considered: radial basis function neural network (RBF-NN)-based estimation and support vector machine (SVM)-based estimation. Based on the system's available observations, the proposed estimation models are used to predict its frequency response. Simulation results are provided to demonstrate the model implementation. Finally, a comparative study is carried out to evaluate the effectiveness of the RBF-NN and SVM schemes, which has demonstrated that the SVM outperformed RBF-NN in the FRF estimation.

Date: 2018
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DOI: 10.1080/00207721.2018.1498555

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