This paper starting from the very first principle presents a derivation of an equation estimating of the final prediction error for a neural network under the recursive least square framework. The equation is in the form: hhPEiF iT 1⁄4 hTEiT N þ d1 N d2 , where d1 and d2 are some values determined by the gradient of the nonlinear mapping at the true system parameter. A cheap way of estimating s...

Efficient matching methods are crucial in Image Processing. In the present paper we outline a novel algorithm of ”stable marriages” that is also fair and globally satisfactory for both populations to be paired. Our applicative examples here being stereo or motion we match primitives based on level lines segments, known for their robustness to contrast changes. They are separately extracted from...

We present a methodology for adaptive ltering and system identi cation under the cyclostationary regime. Our technique is based on a deterministic periodic least-squares criterion, and gives rise to adaptive periodic recursive-least-squares (P-RLS) algorithms. Furthermore, we show that every adaptive RLS algorithm has a P-RLS counterpart, which has exactly the same architecture and the same per...

This paper designs two kinds of recursive least-squares Wiener fixed-point smoothers based on an innovation approach in linear discrete-time stochastic systems. It is assumed that the signal is observed with additive white noise. The proposed fixed-point smoothers require the information of the observation matrix, the system matrix for the state variable, related with the signal, the variance o...

We propose a novel class of efficient adaptive algorithms in the frequency domain that is tailored to very long adaptive filters and highly autocorrelated input signals as they arise, e.g., in highquality full-duplex audio applications. The approach exhibits good tracking capabilities of the signal statistics and very low delay. Moreover, it is shown that the low order of computational complexi...

This paper proposes recursive least-squares (RLS) l-step ahead predictor and filtering algorithms with uncertain observations in linear discrete-time stochastic systems. The observation equation is given by         y k k z k v    k  , , where is a binary switching sequence with conditional probability. The estimators require the information of the system state-transition matrix   ...

Transversal Recursive Least Squares (RLS) algorithms estimate filter coefficients which minimize the accumulated sum of the square of the error residuals termed the error power. In this paper the sensitivity of this error power to random perturbations about the optimum filter coefficients is investigated. Expressions are derived for the mean and variance of the deviation from the optimum error ...

This paper presents distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks. Both incre-mental and diffusion adaptive solutions are all considered. The distributed conventional (CG) and modified CG (MCG) algorithms have an improved performance in terms of mean square error as compared with least-mean square (LMS)-based algorithms and a performance th...

In this paper, we present a method for handling double-talk. This approach uses a robust fast recursive least-squares algorithm (FRLS) and the normalized cross-correlation double-talk detector (NCC DTD). The NCC DTD is developed into a fast version, called FNCC, by reusing computational results from the FRLS algorithm. The major advantage of this detector is that it is much less dependent on th...

In this paper, we present a comparative performance evaluation of adaptive multiuser detectors, including stochastic gradient (SG) and recursive least squares (RLS) algorithms (which require training data), and minimum output energy (MOE) and subspace-based MMSE (S-MMSE) algorithms (which do not require training data), under near-far conditions in a space-time coded CDMA system. We show that, i...