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Description: By building a nonlinear function relationship between an d the error signal，this paper presents a no—
vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．
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Size: 2944 |
Author: 上将 |
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Description: Second and Higher-Order Statistics based Multiple-Input-Multiple-Output System
Blind Identification Matlab Code
-Second and Higher-Order Statistics based Multiple-Input-Multiple-Output System Blin Matlab d Identification Code
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Size: 22528 |
Author: 于蕾 |
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Description: 数字版权保护的毕业设计，利用DCT进行变换，但是算法比较新颖，里面包括所有的matlab程序。-digital copyright protection graduation design, for the use of DCT transformation, but rather novel algorithm, includes all the Matlab procedures.
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Size: 52224 |
Author: 刘伟 |
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Description: By building a nonlinear function relationship between an d the error signal，this paper presents a no—
vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．-By building a nonlinear function relationship between an d the error signal, this paper presents a no-vel variable step size LMS (Least Mean Square) adaptive filtering algorithm.
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Size: 3072 |
Author: 上将 |
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Description: 基于DSP的LMS自适应滤波器的实现
在通用DSP芯片TMS320C5402上实现了基于LMS算法的自适应滤波器，并对调试运行结果进行了分析讨
论，其结果为将来硬件实现LMS自适应滤波器提供了可靠保证，为自适应滤波器在实际中的应用提供了参考-DSP-based LMS adaptive filter realization of common DSP chip TMS320C5402 in realizing algorithm based on LMS adaptive filter, and debug the results are analyzed and discussed, the results for the future hardware implementation of LMS adaptive filter provides a a reliable guarantee for the adaptive filter in the practical application of providing a reference
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Size: 199680 |
Author: 董庆 |
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Description: 实现了DSP2407芯片的A/D-D/A转换，can模块的使用，FFT以及LMS算法-DSP2407 chip achieved A/DD/A conversion, can block the use of, FFT and LMS algorithm
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Size: 17408 |
Author: 刘媛 |
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Description: 包括：三种LMS算法实现AR(2)的预测，法2、3用递推计算Km,两者区别在于d(n)的取法略微不同；用LSL和FTF算法实现简单的系统辨识。-Include: three LMS algorithm AR (2) the forecast, France 2,3 calculated with recursion Km, whichever is the difference between d (n) of a slightly different取法 LSL and the FTF algorithm with simple system identification.
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Size: 975872 |
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Description: Avetis Ioannisyan
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f- Avetis Ioannisyan
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f
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Size: 1024 |
Author: josh |
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Description: 最小均方算法lms在波束形成中的应用 LMS算法步骤： 1,、设置变量和参量： X(n)为输入向量，或称为训练样本 W(n)为权值向量 b(n)为偏差 d(n)为期望输出 y(n)为实际输出 η为学习速率 n为迭代次数 2、初始化，赋给w(0)各一个较小的随机非零值，令n=0 3、对于一组输入样本x(n)和对应的期望输出d，计算 e(n)=d(n)-X^T(n)W(n) W(n+1)=W(n)+ηX(n)e(n) 4、判断是否满足条件，若满足算法结束，若否n增加1，转入第3步继续执行。-Lms least mean square algorithm applied in Beamforming
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Size: 1024 |
Author: 林朝 |
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Description: 1,、设置变量和参量：
X(n)为输入向量，或称为训练样本
W(n)为权值向量
e(n)为偏差
d(n)为期望输出
y(n)为实际输出
η为学习速率
n为迭代次数
2、初始化，赋给w(0)各一个较小的随机非零值，令n=0
3、对于一组输入样本x(n)和对应的期望输出d，计算
e(n)=d(n)-X^T(n)W(n)
W(n+1)=W(n)+ηX(n)e(n)
4、判断是否满足条件，若满足算法结束，若否n增加1，转入第3步继续执行-, set the variables and parameters:
X (n) is the input vector, otherwise known as the training sample
W (n) for the weight vector
e (n) for the deviation
d (n) is the desired output
y (n) is the actual output
η is the learning rate
n is the number of iterations
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Size: 1024 |
Author: 周永辉 |
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Description: Simple function to adjust filter coefficients using the LMS algorithm adjusts filter coefficients, b, to provide the best match between the input, x(n), and a desired waveform, d(n),both waveforms must be the same length, uses a standard FIR filter
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Size: 1024 |
Author: mamoud26 |
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Description: 自适应（LMS）算法在噪声抑制中的应用。在仿真中，原始信号选为sin((0.2*pi)*t),
噪声信号采用标准白噪声，延迟D=1，收敛因子分别是u=0.001和u=0.3-Adaptive (LMS) algorithm in noise suppression application. In the simulation, the original signal is selected sin ((0.2* pi)* t), noise signal using the standard white noise, the delay D = 1, respectively, convergence factor u = 0.001 and u = 0.3
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Size: 8192 |
Author: zhengrui |
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Description: 假设信号产生和传输信道模型为：
而抽头维纳滤波器为：
假设 的方差为0.27， 的方差为0.1，均值都为零。并且：
，
并假设权向量初始值为 ，分别使用步长0.015、0.025和0.05进行 LMS算法仿真。
分析：d(n)是子系统H1受到v1(n)激励产生的信号，而H2与加性噪声构成了加性噪声传输信道。将u(n)作为维纳滤波器的输入，且滤波器的期望响应为d(n)。问题就是如何求出滤波器的权系数使得估计误差e(n)在MMSE意义下最小。
-Estimated channel signal transmission
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Size: 3072 |
Author: 蔡蓉 |
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Description: The antenna is customize by combining the output of individual sensors and they are scaled based on the corresponding weight. And with the help of minimum squared error the LMS can compute the weight . Spatial filtering involves the signal estimation in receiver by reducing the error with the help of reference signal d(t)[5]. And it has some correlation with desired signal and beam former output y(t). The solution is obtained by iteration using LMS algorithm.
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Size: 5120 |
Author: emman |
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Description: FFTlms：需要添加u, y,d,e等数据，该程序就能给出类似于LMS算法的仿真图；
rake：计算误码；
RScode：检验译码是否正确
TCMcode：进行8PSK映射，得到信号点在星座图上的位置
WHTlms读者需要给出输入信号xd, 参考信号d，运行本程序就能得到相应仿真图
-FFTlms: you need to add u, y, d, e and other data, the program will be able to give similar LMS algorithm simulation map rake: calculation errors RScode: test decode correctly TCMcode: 8PSK mapping performed to obtain a signal point in the constellation position WHTlms readers need to give the input signal xd, the reference signal d, will be able to run this program to give the corresponding simulation map
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Size: 4096 |
Author: pp |
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Description: 用MATLAB编写的lms算法，设置变量和参量，赋，对于一组输入样本x(n)和对应的期望输出d-MATLAB prepared by the LMS algorithm, set variables and parameters, Fu, for a set of input samples x (n) and the corresponding expected output D
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Size: 1024 |
Author: 江东 |
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Description: 調適性訊號處理
考慮應用於自適應最小方差無失真響應（MDVR）波束成形器的LMS算法由五個均勻間隔的傳感器的線性陣列組成。 陣列的相鄰元件之間的間隔d等於接收波長λ的一半。 波束形成器在由兩個分量組成的環境中操作：沿著感興趣的方向撞擊陣列的目標信號和來自
未知方向。 假設這兩個分量源自獨立的源，並且接收的信號在每個傳感器的輸出處包括加性白高斯噪聲。-ASP
Consider the LMS algorithm applied to an adaptive minimum-variance distortionless response (MDVR) beamformer consists of a linear array of five uniformly spaced sensors. The spacing d between adjacent elements of the array equals one-half of the received wavelength . The beamformer operates in an environment that consists of two components: a target signal impinging on the array along a direction of interest and a single source of interference coming an
unknown direction. It is assumed that these two components originate independent sources and that the received signal includes additive white Gaussian noise at the output of each sensor.
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Size: 162816 |
Author: 張瑀征 |
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Description: 核心代码为 "mlms.m" ，其基础为LMS算法（最小均方差算法）。 "demon_mlms.m"演示了算法的收敛性能，而 "De50HzInterference.m" 则给出了其在去50Hz工频干扰方面的应用。当然，算法本身还可以应用在其他方面，比如回声消除等，只需要在此基础上做出相应的修改即可以应用在自己的工程上。(The core file is "mlms.m". it's modified LMS method with good astringency.
The file "demon_mlms.m" just shows its astringency.
The file "De50HzInterference.m" shows how to remove labor frequency interference of 50Hz.
"d.dat" and "fs.data" are the speech data and its sampling freqency that "De50HzInterference.m" would use.
Just run "demon_mlms.m" and "De50HzInterference.m" to get the idea of how mlms works.
Have fun ! :))
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Size: 66560 |
Author: ZhaoYiMing
|
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Description: The antenna is customize by combining the output of individual sensors and they are scaled based on the corresponding weight. And with the help of minimum squared error the LMS can compute the weight . Spatial filtering involves the signal estimation in receiver by reducing the error with the help of reference signal d(t)[5]. And it has some correlation with desired signal and beam former output y(t). The solution is obtained by iteration using LMS algorithm.
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Size: 2048 |
Author: prajansid |
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