Abstract
A huge class of nonlinear dynamic systems can be approximated by the Nonlinear AutoRegressive with eXogenous inputs (NARX) models. This paper proposes a novel method, Sparse Augmented Lagrangian (SAL), for NARX model variable selection and parameter estimation. Firstly, Split Augmented Lagrangian Shrinkage Algorithm (SALSA) is applied to produce some intermediate models with subsampling technique, and then only the model terms with high selecting probability are chosen into the final model, followed by the model parameter estimation via SALSA. The model sparsity and algorithm convergence can be guaranteed through theoretical analysis. Two nonlinear examples and one real-world application from the process industry are used to demonstrate the effectiveness and advantages of the proposed method in comparison to several popular methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
