Abstract
Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with eXogenous inputs (NARX) models of a nonlinear system. This algorithm, which is derived from the Matching Pursuit algorithm, is used for the online identification of nonlinear dynamic systems. The NARX model is expanded into a sum of non-orthogonal spline basis functions. The convergence of the algorithm is proved for certain signal assumptions. Simulation experiments are provided for examples previously solved in the literature.
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.
