The recursive least squares (RLS) algorithm is well known and has been widely used for many years. Most analyses of RLS have assumed statistical properties of the data or the noise process, but recent robust H∞ analyses have been used to bound the ratio of the performance of the algorithm to the total noise. In this paper, we provide an additive analysis bounding the difference between performa...

Exponentially weighted recursive least-squares (RLS) algorithms are commonly used for fast adaptation. In many cases the input signals are continuous-time. Either a fully analog implementation of the RLS algorithm is applied or the input data are sampled by analog-to-digital (AD) converters to be processed digitally. Although a digital realization is usually the preferred choice, it becomes unf...

A new algorithm, multiple concurrent recursive least squares (MCRLS) is developed for parameter estimation in a system having a set of governing equations describing its behavior that cannot be manipulated into a form allowing (direct) linear regression of the unknown parameters. In this algorithm, the single nonlinear problem is segmented into two or more separate linear problems, thereby enab...

Recursive estimates of large systems of equations in the context of least squares fitting is a common practice in different fields of study. For example, recursive adaptive filtering is extensively used in signal processing and control applications. The necessity of solving least squares problem via recursive algorithms comes from the need of fast real-time signal processing strategies. Computa...

This paper shows that the recursive least-squares (RLS) algorithm with forgetting factor is a special case of a varying-coe$cient model, and a model which can easily be estimated via simple local regression. This observation allows us to formulate a new method which retains the RLS algorithm, but extends the algorithm by including polynomial approximations. Simulation results are provided, whic...

The problem of detection and discrimination of double talk and change in the echo path in a telephone channel is consid ered A change in echo path requires fast adaptation of the channel model to be able to equalize the echo dynamics On the other hand the adaption rate should be reduced when double talk occurs Thus it is critical to quickly detect a change in echo path while not confusing it wi...

This paper proposes a technique for tuning of a discrete adaptive controller that is designed based on Lyapunov stability concepts. The tuning is based on the minimization of a performance index that can be calculated from a generalized eigenvalue problem (GEVP). The resulting controller, tuned with the proposed methodology, provides better performance than an adaptive controller based on a Rec...

For the lifted input–output representation of general dual-rate sampled-data systems, this paper presents a decomposition based recursive least squares (D-LS) identification algorithm using the hierarchical identification principle. Compared with the recursive least squares (RLS) algorithm, the proposed D-LS algorithmdoes not require computing the covariancematriceswith large sizes andmatrix in...

In this paper an adaptive minimum variance controller is proposed to minimize the rate of stochastic inputs from uncontrollable high priority sources. This method avoids the computations needed for pole placement design of the minimum variance controller, and utilizes an online recursive least squares algorithm in direct tuning of the controller parameters.

This paper introduces a dynamically regularized fast recursive least squares (DR-FRLS) adaptive filtering algorithm. Numerically stabilized FRLS algorithms exhibit reliable and fast convergence with low complexity even when the excitation signal is highly self-correlated. FRLS still suffers from instability, however, when the condition number of the implicit excitation sample covariance matrix ...