In this short note, a direct proof of L2 convergence of an Euler–Maruyama approximation of a Zakai equation driven by a square integrable martingale is shown. The order of convergence is as known for real-valued stochastic differential equations and for less general driving noises O( √ ∆t) for a time discretization step size ∆t.

in this paper, economic convergence in selected oic (36 countries) is studied. the convergence hypothesis is tested through three approaches: beta convergence, sigma convergence, and time series model. ols is used for estimating the cross section model and cross variance is used to test the distributive model. for analyzing time series model, we used augmented dicky fuller test. beta convergenc...

The application of the least-mean-square (LMS) and recursive-least-square (RLS) algorithms to the estimation of symbol period is discussed. The algorithms are based on the measurements of time between two consecutive detected transitions in noisy waveforms. Two versions of the algorithm are developed, for white and colored measurement noise model. Conditions are derived that guarantee proper be...

lorentz characterized the almost convergence through the concept of uniform convergence of de lavallée-poussin mean. in this paper, we generalize the notion of almost convergence by using the concept ofinvariant mean and the generalized de la vallée-poussin mean. we determine the bounded linear operators forthe generalized σ-conservative, σ-regular and σ-coercive matrices.

This paper proposes a novel adaptive filtering algorithm which converges faster than the Krylov proportionate normalized least mean square (KPNLMS) algorithm. KPNLMS is known to exhibit faster convergence than the standard NLMS algorithm for all unknown systems. Our algorithm is named Krylov proportionate normalized least mean fourth (KPNLMF) and it deals with mean fourth minimization of the er...

This paper presents first and second order convergence analysis of the sparsity aware l0-RLS adaptive filter. The theorems 1 and 2 state the steady state value of mean and mean square deviation of the adaptive filter weight vector.