منابع مشابه
Constrained adaptive LMS L-filters
Two novel adaptive nonlinear filter structures are proposed which are based on linear combinations of order statistics. These adaptive schemes are modifications of the standard LMS algorithm and have the ability to incorporate constraints imposed on coefficients in order to permit location-invariant and unbiased estimation of a constant signal in the presence of additive white noise. The conver...
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The LMS-based adaptive order-statistic filters are presented in this paper. The adaptive Ll-filters as extension of the adaptive L-filter for two-dimensional filtering of noisy greyscale images is studied too. Their adaptation properties are studied by three types of noise, the additive white Gaussian noise, the impulsive noise or both, respectively. Moreover, the impulsive noise has the fixed ...
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In the WINDS project we have develo ped the ALE learning management system to support Architecture and Civil Engineering Design Courses. The ALE system integrates the functionality of an e -Learning system with adaptive educational hypermedia on the Web. Our plan is to further develop the system towards mobile learning in the new RAFT project.
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Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal-to-noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is ...
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In this paper we concentrate on the problem of comparing the effectiveness of the Darwinian, and Least Square methods, with the influence of the input signal (statistics) spreadness highlighted. Our approach in this paper is to carry out an extensive suite of simulations for both the Darwinian algorithm and LMS algorithm under varying initial conditions and eigenvalue spread of the signal autoc...
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ژورنال
عنوان ژورنال: «System analysis and applied information science»
سال: 2019
ISSN: 2414-0481,2309-4923
DOI: 10.21122/2309-4923-2019-2-58-64