Partial reverse concatenation for data storage
Roy D. Cideciyan, Robert Hutchins, et al.
APSIPA 2014
A class of exact fast algorithms originally introduced in the signal processing area is provided by the so-called recursive least squares ladder forms. The many nice numerical and structural properties of these algorithms have made them a very powerful alternative in a large variety of applications, yet the convergence properties of the algorithms have not received the necessary attention. This paper gives an asymptotic analysis of two ladder algorithms, designed for autoregressive (AR) and autoregressive moving average (ARMA) models. Convergence is studied based on the stability properties of an associated differential equation. It is shown that the convergence conditions obtained for the algorithms parallel those known for prediction error methods and for a particular type of pseudo-linear regression. © 1986.
Roy D. Cideciyan, Robert Hutchins, et al.
APSIPA 2014
Ajay Dholakia, Sedat Ölçer
IEEE ICC 2004
Roy D. Cideciyan, Sedat Ölçer
ISIT 1991
Giovanni Cherubini, Evangelos Eleftheriou, et al.
IEEE Communications Magazine