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LMSFilterMatlab
- 此程序可实现基于LMS（最小均方误差算法）的自适应滤波程序-this procedure can be based on the LMS (least-mean-square error algorithm) adaptive filtering process
adaptive_interference_cancellation_with_LMS_algor
- this is a code for adaptive interfrence cancellation on a certain signal using the least mean square algorithm LMS using matlab.-this is a code for adaptive interfrence cancellation on a certain signal using the least mean square algorithm LMS using
LMS
- 采用一种快速收敛变步长LMS（Least mean square ） 自适应最小均方算法matlab源程序，其中算法所做的工作是用FIR 滤波器的预测系统，对IIR系统进行预测，如果阶数越高越能逼近被预测系统。-Using a fast convergence of variable step size LMS (Least mean square) adaptive least mean square algorithm matlab source, one of algorithm is t
nlms
- Normalized Least mean square algorithm in matlab
LMS
- LMS: Least Mean Square the source code for state space environment.
LMS
- least mean square algorithm for estimation state
LMS-MATLAB
- LMS-MATLAB最小均方算法的Matlab源程序,模式识别中的分类器-LMS-MATLAB least-mean-square algorithm of Matlab source code, Pattern Recognition Classifier
Adaptive_Filters_Theory_and_Applications
- Least Mean Square Newton Algorithm
Least-square-filtering
- 这个包中包含学习最小均方滤波的一个例子及其和其它滤波方法的一些比较。-This package contains the learning of least mean square with an example. And it compared least mean square method with other filtering methods.
KLTLMS
- Karhunen Loeve Least Mean Square Algorithm for mobile communications 3G
lms1
- 智能天线方向图——波束形成最小均方(LMS)算法-Smart antenna pattern- beam forming least mean square (LMS) algorithm
LMS
- Least Mean Square (LMS) equalizer used in coherent receivers
ENEE634_report1
- Least Mean Square algorithm
s882211MatlabProject
- Least Mean Square using matlab
lms2
- this algorithm based on least mean square -this is algorithm based on least mean square
LMS
- LMS(Least Mean Square)算法是一种应用最为广泛的最优化算法-LMS (Least Mean Square) algorithm is a most widely used optimization algorithm
dsp.least.mean.square.algorithm
- 数字信号处理自适应数字滤波器最小均方算法的结构与运算-DSP least mean square algorithm
HDLImplementationoftheVariableStepSize
- proposes a Verilog implementation of the Normalized Least Mean Square (NLMS) adaptive algorithm, having a variable step size. The envisaged application is the identification of an unknown system. First the convergence of derived LMS algorithm
leastsquare
- 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
Least Mean Square for System Identification
- Least Mean Square for System Identification