This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with forgetting factor. A simplified first-order differential equation of the system was designed its parameters were estimated in real-time using RLS algorithm. The steady-state input for target-state tracking derived based on perfor...

An approximation for the constant modulus (CM) cost function is proposed to allow the use of the fast recursive least squares (RLS) algorithm. Simulations are performed to compare the performance of the introduced RLS-CM and stochastic gradient descent (SGD) algorithms for blind adaptive beamforming. Results indicate that the introduced RLS-CM has faster convergence speed and good tracking abil...

In this contribution it is shown that the sensitivity analysis predicts that the mean square excess error is the same for both correlated and white inputs. Correlation increases the variance about the mean square excess error. We also present a stable finite precision RLS algorithm. Many adaptive filtering problems can be cast as a systems identification problem. Consider the desired signal d(n...

This paper designs the recursive least-squares (RLS) Wiener finite impulse response (FIR) predictor and filter, based on the innovation approach, in linear discrete-time stochastic systems. It is assumed that the signal is observed with additive white noise and the signal process is uncorrelated with the observation noise process. This paper also presents the recursive algorithms for the estima...

| In this paper we exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered, one pertaining to a system identiication problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applicatio...

Cold orbital forging (COF) as an advanced incremental metal-forming technology has been widely used in processing vehicle parts. During the COF process, vibration on machine injures service life of and quality forged part. The study control is therefore necessary. In this study, dynamic model established, performances some key positions are obtained using Matlab&Simulink software. Subsequen...

a r t i c l e i n f o a b s t r a c t Keywords: EEG/ERP Adaptive filter SNR LMS RLS ABC In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). ERP generated due to hand movement is filtered through...

An online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm...

This paper presents a Least Absolute Value (LAV) state estimator for power system using a new method called Recursive Least Squares (RLS). Also, a suitable model is used for state estimation of power system that includes a Unified Power Flow Controller (UPFC). The IEEE 14-bus test system is used to show the validity of the proposed algorithm in estimation of power system states as well as the s...

Recursive Least Squares (RLS) algorithms have wide-spread applications in many areas, such as real-time signal processing, control and communications. This paper shows that the unique solutions to linear-equality constrained and the unconstrained LS problems, respectively, always have exactly the same recursive form. Their only difference lies in the initial values. Based on this, a recursive a...