This paper deals with the performance of the recursive least squares algorithm when it is applied to problems where the measured signal is corrupted by bounded noise. Using ideas from bounding ellipsoid algorithms we derive an asymptotic expression for the bound on the uncertainty of the parameter estimate for a simple choice of design variables. This bound is also transformed to a bound on the...

The identification of continuous-time stochastic systems, in particular recursive estimation, is a basic building block for continuous-time stochastic adaptive filtering and control as well, see the works of Van Schuppen, Duncan and Pasik-Duncan. In these papers the underlying stochastic systems is essentially an AR-system, for which the recursive maximum-likelihood (RML) estimation reduces to ...

A new non-linear Recursive Least Squares (RLS) algorithm is presented in the context of pattern classification problems. The algorithm incorporates the non-linearity of the filter’s output in the updating rules of the classical RLS algorithm. The proposed method yields lower stationary error levels when compared to the standard LMS and RLS algorithms in a classical application of pattern classi...

Earlier research on optimal monetary policy under learning uses optimality conditions derived under rational expectations. In this paper instead, we derive optimal monetary policy when the central bank knows the algorithm followed by agents to form their expectations and makes active use of the learning behavior. There is a well known intratemporal tradeoff between inflation and output gap stab...

The adaptive Volterra filter (AVF) corresponding to nonlinear system is attractive due to its linear relationship between the input and output signals. However, the formidable computer complexity of AVF is prohibitive for its practical applications. This paper presents a design method of noise canceller by using a parallel fast recursive least squares (RLS) adaptive Volterra filter (PAVF). PAVF...

This paper presents theoretical and experimental results of a newly developed automatic controller tuning algorithm called Robust Estimation for Automatic Controller Tuning (REACT) to tune a linear feedback controller to the unknown spectrum of disturbances present in a feedback loop. With model uncertainty and controller perturbations described in (dual) Youla parametrizations, the REACT algor...

This paper deals with diagonal variable loading recursive least squares (VLRLS) array beamforming based on a generalized sidelobe canceller. In conjunction with a subweight partition approach, the VLRLS-based beamformer demonstrates the advantages of fast convergence speed, insensitivity to dynamic estimate modeling error, less computational load, and more robust to against pointing errors and ...

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...

Proportional control methods of controlling congestion in high speed ATM networks fail to achieve the desired performance due to the action delays, nonlinearities, and uncertainties in control loop. 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 ...

Reportedly, guaranteeing the controllability of the estimated system is a crucial problem in adaptive control. In this paper, we introduce a recursive least squares-based identiication algorithm for stochastic SISO systems , which secures the uniform controllability of the estimated system and presents closed-loop identiica-tion properties similar to those of the least squares algorithm. The pr...