Fast algorithms for recursive identification

Recursive identification algorithms are of great interest in control and estimation problems, and related areas such as recursive least squares- and adaptive methods. Recently we have shown how a certain shift invariance inherent in many estimation and control problems can be exploited to obtain fast algorithms that often require orders of magnitude less computations than presently available methods to compute optimal gains. We have developped fast algorithms for a variety of presently available identification methods as well as new ones that require computer time and storage (or hardware) per measurement only proportional to the number of model parameters, compared to the square of the number of parameters for previous methods. Since parameter identification can be formulated as a state estimation problem, optimal filtering results can be applied. In particular we would like to attract attention to alternatives, such as square-root methods and their last versions or ladder forms using partial correlations that have several computational and numerical advantages over the more commonly used methods.