Title :
An upper bound for the recursive least squares estimation error
Author :
Niederlinski, A.
Author_Institution :
Inst. Autom., Politech. Slaska, Gliwice
fDate :
9/1/1995 12:00:00 AM
Abstract :
A new upper bound for the convergence rate of recursive least squares (RLS) errors is presented. The bound is free of some deficiencies of a cell-known RLS upper bound and allows a realistic assessment of factors influencing convergence rate, such as input-output data scaling, disturbances, signal-to-noise ratio, number of estimated parameters, data discounting, and excitation properties of plant inputs. Some of the properties of the new bound are discussed
Keywords :
convergence of numerical methods; differential equations; eigenvalues and eigenfunctions; error statistics; least squares approximations; parameter estimation; stochastic processes; S/N ratio; SISO ARX plants; convergence rate; data discounting; eigenvalue; estimation error; input-output data scaling; parameter estimation; recursive least squares error; stochastic difference equation; upper bound; Convergence; Estimation error; Least squares approximation; Lyapunov method; MIMO; Parameter estimation; Resonance light scattering; Signal to noise ratio; Upper bound; White noise;
Journal_Title :
Automatic Control, IEEE Transactions on
