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System identification with adaptive filter using full and partial-update Transform-Domain Least-Mean-Squares ,System identification with adaptive filter using full and partial-update Transform-Domain Least-Mean-Squares
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Beamforming thesis describing Study of a various Beamforming Techniques And Implementation of the Constrained Least Mean Squares (LMS) algorithm for Beamforming
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System identification with adaptive filter using full and partial-update Generalised-Sideband-Decomposition Least-Mean-Squares
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System identification with adaptive filter using full and partial-update Least-Mean-Squares -System identification with adaptive filter using full and partial-update Least-Mean-Squares
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System identification with adaptive filter using full and partial-update Normalised-Least-Mean-Squares
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这是一篇关于椭圆检测的文章:对于做人脸识别,模式识别的同行应该会有所帮助.找了好久,才下到,上传来共享下.-abstract:The Hough transformation can detect straight lines in an edge-enhanced picture, however its extension torecover ellipses
requires too long a computing time. This correspondence proposes
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Filter design based on least mean squares algorithm.
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均衡技术是克服码间干扰(Inter-Symbol Interference,ISI)的有效措施,由于信道特性的随机性与时变性,实际中消除码间干扰最常用的是自适应均衡器。本文对基于最小均方(Least Mean Squares,LMS)算法和递推最小二乘(Recursive Least Squares,RLS)算法的自适应均衡器进行仿真研究,分析了信道特性与设计参数对自适应均衡器的收敛速度与稳态性能的影响。
-Equalization technique is to overcome inte
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matlab的各种自适应仿真分析。。自适应信息处理的算法、方案繁多，究其实质可归纳为遵循最小均方误差(Least Mean Square，LMS)准则及最小二乘(Least Square，LS)准则两大类，其他算法大多是这两种算法的演进。-matlab simulation analysis of various adaptive. . Adaptive information processing algorithms, a variety of plans, their essence ca
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LMS: Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual
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LMS: Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual
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Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual sign
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Least mean squares algorithm for smart antenna
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Inter-symbol interference if not taken care off may
cause severe error at the receiver and the detection of signal becomes
difficult. An adaptive equalizer employing Recursive Least Squares
algorithm can be a good compensation for the ISI probl
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本文介绍了自适应算法及其结构，主要以LMS算法为例。介绍了自适应算法应用的领域。-The book shows the theory of adaptive signal processing.The least mean squares (LMS) method is used as an example for showing the adaptive algorithm.
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fitting function for data curve fitting using least mean square
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Matlab Code to filter data using the Least Mean Squares adaptive algorithm. processes an input WAV file and filters out noise according to the setup parameters
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An Adaptive Block-Based Eigenvector Equalization for Time-Varying Multipath Fading Channels.In this paper we present an adaptive Block-
Based EigenVector Algorithm (BBEVA) for blind equalization
of time-varying multipath fading channels. In addit
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LMS algorithm for adaptive filter least mean squares-LMS algorithm for adaptive filter least mean squares
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Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual si
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