Abstract
Numerical procedures for dynamic system identification are discussed. Efficient algorithms for static least-squares problems provide a starting point for dynamic systems nonlinear programming methods. This paper shows that in dynamic systems, the additional computation time required for the first and the second gradients over function evaluation is small compared to static systems. This makes gradient procedures very attractive for dynamic system parameter estimation. Additional simplifications are made for linear systems. Finally, some practical simplifications are suggested to enable identification in large scale systems using current computers.
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.
