Abstract
In this paper, a direct weight optimization method is proposed for nonlinear system identification based on the minimal probability idea. The approach has several quite attractive features and is very different from existing ones. It is optimal for any given number of finite data points and at the same time possesses asymptotic convergence. The estimator admits a closed form and no numerical optimization is needed. Theoretical analysis and numerical simulations show that the approach is a very competitive alternative to existing nonlinear identification 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.
