Abstract
The relationship between input and output of many nonlinear dynamic systems can be modeled as the Nonlinear Auto-Regressive model with exogenous inputs (NARX model). Identification of the NARX model plays a vital role in system modelling based on NARX models. This paper compares three system identification approaches for the NARX model, including the support vector machine (SVM), the least-square support vector machine and the forward regression orthogonal estimator. Statistical criteria of mean-square error for the one-step-ahead prediction and the model predicted output are used to evaluate the performance of those identification approaches. Simulations of a six degrees of freedom nonlinear system under different levels of noise excitation are given. It seems advisable to choose the SVM for identifying the NARX model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.