On least squares algorithms for system parameter identification

A new least squares solution for obtaining asymptotically unbiased and consistent estimates of unknown parameters in noisy linear systems is presented. The proposed algorithms are in many ways more advantageous than generalized least squares algorithm. Extensions to on-line and multivariable problems can be easily implemented. Examples are given to illustrate the performance of these new algorithms.