Adaptive Algorithm via a Truncated Least Squares Method
Yoshihiro Yamamoto · Transactions of the Society of Instrument and Control Engineers · 1990
A truncated least squares method (TLSM) is proposed for the on-line parameter estimation of linear regression models.The method depends on the truncated data which is a collection of the last M observations.Here, M is any number greater than N which is a number of unknown parameters in a regression model.With a suitable initial setting, the algorithm always satisfies the normal equation of the TLSM.Under the ideal circumstance where there is no uncertainty, the algorithm converges to its true value in a finite steps M. So, if M equals to N, the TLSM gives a minimum steps estimator.The case M=N coincides with the orthogonal projection adaptive algorithm proposed by the author.