are applied to test image. The proposed method includes

fuzzy clustering part for clustering the observed vector of each pixel into one of M mutually exclusive

blocks. In the training phase, Least Mean Square (LMS) algorithm use to train center...

Abstract: Data reusing normalized least mean

squares (DRNLMS) algorithms converge often faster

than the conventional least mean squares (LMS)

algorithm. This paper analyzes an adaptive DRNLMS,

ADRNLMS, algorithm which has...

In this paper, a new median filter switcher is presented for suppression of impulsive noise

in gray-level images. The proposed filter is Modified Adaptive Center Weighted Median

(MACWM) filter with an adjustable ...

This paper presents a new method based on the

use of an optimization approach along with kernel least

mean square (KLMS) algorithm for solving ordinary dif-

ferential equations (ODEs). The ...

.

To remedy the filtering degradation problem, a robust filter named as the KFLMS,

which has good noise rejection property for such systems, is developed in this paper,

where the UI estimates are obtained by using least mean square algorithm...

, called Neural Network Kernel

Least Mean Square1, attempts to fuse outputs of the

ensembles of classifiers. The proposed Neural Network

has some special properties such as Kernel abilities,

Least Mean Square features, easy...