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...

This paper proposes a cascaded RLS-LMS predictor for lossless audio coding. In this proposed predictor, a high-order LMS predictor is employed to model the ample tonal and harmonic components of the audio signal for optimal prediction gain performance. To solve the slow convergence problem of the LMS algorithm with colored inputs, a low-order RLS predictor is cascaded prior to the LMS predictor...

This paper considers an extended recursive least squares (RLS) adaptive bilinear predictor. It is shown that the extended RLS adaptive bilinear predictor is guaranteed to be stable in the sense that the time average of the squared a-posteriori prediction error signal is bounded whenever the input signal is bounded in the same sense. It also shows that the a-priori prediction error itself is bou...

This paper introduces a cascade d RLS-LMS predictor for predictive lossless audio coding applications. In this proposed predictor, a LMS predictor with large order is employed in order to capture the ample tonal and harmonic components of the audio signal for optimal prediction gain performance. Meanwhile, to solve the slow convergence problem of the LMS algorithm with colored inputs, a low-ord...

This paper shows that it is possible for an adaptive transversal prediction filter to outperform the fixed Wiener predictor of the same length for narrowband input signal embedded in Added White Gaussian Noise (AWGN). The error transfer function approach, which takes into account of the correlation of predictor error feedback and input signal, is derived for stationary and chirped input signals...

We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal `1,∞-penalized recursive least squares (RLS) predictor. Each update minimizes a convex but nondifferentiable function optimization ...

After observing that several families with essential tremor (ET) clinically cosegregated with restless legs syndrome (RLS), we prospectively evaluated for the presence of RLS in 100 patients presenting to the Baylor College of Medicine with ET and prospectively examined all patients presenting with RLS for the presence of tremor during the same time frame. Of 100 consecutive ET patients (60 wom...

in this paper, an interactive model for individual normal behaviour of drivers is presented in which the mutual effect of vehicles has been incorporated. temporal features obtained from vehicles tracking and their motion history is utilized for generating a model of normal behaviour. because of non-stationarity of behaviour, hidden markov model has been used for interactive model. this model ha...

A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive non-linear forward predictor based on a Pipelined Recurrent Neural Network (PRNN). Simulation studies with speech signals show that the non-linear predictor does not perform satisfactorily when the previously proposed stochastic gradient (SG) algorithm is used. However, significantly improved resul...