Title :
A stopping rule for least-squares identification
Author_Institution :
Dept. of Math., Wayne State Univ., Detroit, MI, USA
fDate :
6/1/1989 12:00:00 AM
Abstract :
A stopping rule for least-squares identification is developed. The stopping rule is determined by the construction of a confidence ellipsoid. For any predetermined estimation error ε>0, if the iterates are inside of an ellipsoidal confidence region with volume less than or equal to εr, then the recursive online algorithm will be terminated with high probability
Keywords :
identification; least squares approximations; confidence ellipsoid; least-squares identification; recursive online algorithm; stopping rule; Adaptive control; Convergence; Ellipsoids; Estimation error; Linear systems; Mathematics; Parameter estimation; Recursive estimation;
Journal_Title :
Automatic Control, IEEE Transactions on
