New Prediction-Augmented Classical Least-Squares (PACLS) Methods: Application to Unmodeled Interferents

Structural stability of least squares prediction methods

A structural stability condition is sought for least squares linear prediction methods in the given data case. Save the Toeplitz case, the structure of the normal equation matrix yields no acknowledged guarantee of stability. Here, a new sufficient condition is provided, and several least squares prediction methods are shown to be structurally stable.

Application of Autocovariance Least - Squares Methods to Laboratory Data ∗

The purpose of this paper is to demonstrate the autocovariance least-squares (ALS) techniques to a laboratory reactor for the conversion of acetic anhydride to acetic acid. In this way, it is demonstrated that the methods proposed by Odelson and Rawlings are applicable to actual process data, and not just theoretical simulations. A variety of control scenarios are tested including: simple regul...

An Estimation Of Seasonal GDP Gap In Iran: Application Of Adaptive Least Squares Methods

Total least squares methods

1. the use of weighted norms as a measure of the data perturbation size, capturing prior knowledge about uncertainty in the data; 2. the addition of constraints on the perturbation to preserve the structure of the data matrix, motivated by structured data matrices occurring in signal and image processing, systems and control, and computer algebra; 3. the use of regularization in the problem for...

Universal Linear Least - Squares Prediction

An approach to the problem of linear prediction is discussed that is based on recent developments in the universal coding and computational learning theory literature. This development provides a novel perspective on the adaptive filtering problem, and represents a significant departure from traditional adaptive filtering methodologies. In this context, we demonstrate a sequential algorithm for...