Abstract
So far, we have worked with the “fixed-order” formulation of the Normal Equations, i.e., we have set the order of the Normal Equations to a fixed value before solving them. The solution algorithms which required such a fixed-order description of the linear least-squares prediction problem were merely based on algebraic concepts, employing orthogonal rotations (as in the case of the QRLS algorithms based on the recursive Givens reduction), or on the Sherman-Morrison identity in connection with a transversal prediction error filter.
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.
