Employing a recently introduced framework, within which a large number of classical and modern adaptive lter algorithms can be viewed as special cases, a generic, variable step-size adaptive lter has been presented. Variable Step-Size (VSS) Normalized Least Mean Square (VSSNLMS) and VSS A ne Projection Algorithms (VSSAPA) are particular examples of adaptive algorithms covered by this generic va...

Convergence rate of an algorithm is an important factor that determines the deployment of such algorithm in a real time application. In this paper, we propose improved versions of normalized least mean square (NLMS) algorithm: single and multiple -variable step size normalized least mean square (VSSNLMS) algorithms for echo cancellation. The presented algorithms exhibit faster convergence rate ...

Though the Leaky Least-Mean-Square (LLMS) algorithm mitigates the drifting problem of the LMS algorithm, its performance is similar to that of the LMS algorithm in terms of convergence rate. In this paper, we propose a new LLMS algorithm that has a better performance than the LLMS algorithm in terms of the convergence rate and at the same time solves the drifting problem in the LMS algorithm. T...

In this paper, we are interested in the numerical solutions of stochastic functional differential equations (SFDEs) with jumps. Under the global Lipschitz condition, we show that the pth moment convergence of the Euler-Maruyama (EM) numerical solutions to SFDEs with jumps has order 1/p for any p ≥ 2. This is significantly different from the case of SFDEs without jumps where the order is 1/2 for...

Adaptive least mean square (LMS) filters with or without training sequences, which are known as training-based and blind detectors respectively, have been formulated to counter interference in CDMA systems. The convergence characteristics of these two LMS detectors are analyzed and compared in this paper. We show that the blind detector is superior to the training-based detector with respect to...

A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...