Showing papers on "Recursive least squares filter published in 1969"

Extensions and applications of the Householder algorithm for solving linear least squares problems

Abstract: Mathematical and numerical least squares solution of linear equations, using Householder algorithm

An adaptive recursive-least-squares identification algorithm

Abstract: A new algorithm for identification from input-output measurements of a canonical form for discrete-time linear systems with disturbances having rational spectral densities is obtained by a formal application of the recursive least squares formula. Although in this case the assumptions of the least squares method are violated, the algorithm is shown to converge in mean square using a stochastic approximation proof. The proposed algorithm is computationally more expensive than the corresponding stochastic approximation formula [1], but converges much faster and there are no problems with choice of the gain constant. The complexity of the algorithm still compares favourably with other methods [2], [3], owing to its on-line structure.

Least Squares Made Easy.

Abstract: A very practical method for hand calculation of least-squares-adjusted slopes, acceleration coefficients, etc., is presented and compared with better known but less-satisfactory algorithms. Particular attention is given to easy methods for estimating the precision of the adjusted constants. The method, based on weighted successive differences, applies to the special case of equal increments in the independent variable.

An experimental comparison of several approaches to the linear least squares problem

Abstract: Several computational algarithms for obtaining least square solutions to a system of equations are made. The a l gorithms include both conventional and pseudo inversion methods. Comparisons of running time, computer storage and accuracy of recovery are made for each algorithm in both single and doub16 precision.

On an identification method for linear discrete systems

Abstract: The problem of linear discrete system parameter identification in the presence of noise is studied An existing technique used in adaptive systems is examined and modification is made to solve the identification problem in the open-loop configuration