نتایج جستجو برای: LMS algorithm
تعداد نتایج: 757726 فیلتر نتایج به سال:
This paper presents a simple and easy implementable Least Mean Square (LMS) type approach for frequency estimation of three phase power system in an unbalanced condition. The proposed LMS type algorithm is based on a second order recursion for the complex voltage derived from Clarke's transformation which is proved in the paper. The proposed algorithm is real adaptive filter with real parameter...
In this paper, echo cancellation is done using genetic algorithm (GA). The genetic algorithm is implemented by two kinds of crossovers; heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and diff...
The Least Mean Mixed-Norm (LMMN) algorithm is a stochastic gradient-based algorithm whose objective is to minimum a combination of the cost functions of the Least Mean Square (LMS) and Least Mean Fourth (LMF) algorithms. This algorithm has inherited many properties and advantages of the LMS and LMF algorithms and mitigated their weaknesses in some ways. The main issue of the LMMN algorithm is t...
This paper presents new adaptive filtering techniques used in speech enhancement system. Adaptive filtering schemes are subjected to different trade-offs regarding their steady-state misadjustment, speed of convergence, and tracking performance. Fractional Least-Mean-Square (FLMS) is a new adaptive algorithm which has better performance than the conventional LMS algorithm. Normalization of LMS ...
A new adaptive lter algorithm has been developed that combines the beneets of the Least Mean Square (LMS) and Least Mean Fourth (LMF) methods. This algorithm , called LMS/F, outperforms the standard LMS algorithm judging either constant convergence rate or constant misadjustment. While LMF outperforms LMS for certain noise prooles, its stability cannot be guaranteed for known input signals even...
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