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Description: 基于lms算法的X-LMS算法，比较适合实际模型
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Size: 907 |
Author: 安哲 |
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Description: 针对TI公司的DSK6711所发展的适应性调适范例，包含完整LMS功能，CCS2.x版，采用C语言完整编译-against TI's DSK6711 developed adaptive adjustment example, LMS includes functional integrity, CCS2.x version, complete with C language compiler
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Size: 19456 |
Author: Tony |
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Description: 基于lms算法的X-LMS算法，比较适合实际模型-LMS algorithm based on X-LMS algorithm, more suitable for the actual model
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Author: 安哲 |
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Description: 若不希望用与估计输入信号矢量有关的相关矩阵来加快LMS算法的收敛速度，那么可用变步长方法来缩短其自适应收敛过程，其中一个主要的方法是归一化LMS算法（NLMS算法），变步长 的更新公式可写成
W（n+1）=w（n）+ e（n）x（n）
=w（n）+ （3.1）
式中， = e（n）x（n）表示滤波权矢量迭代更新的调整量。为了达到快速收敛的目的，必须合适的选择变步长 的值，一个可能策略是尽可能多地减少瞬时平方误差，即用瞬时平方误差作为均方误差的MSE简单估计，这也是LMS算法的基本思想。
-Want to estimate if the input signal vector and the relevant matrix to speed up the convergence rate of LMS algorithm, then the variable step size method can be used to shorten its adaptive convergence process, one of the main method is normalized LMS algorithm (NLMS algorithm) , variable step-size update formula can be written W (n+ 1) = w (n)+ e (n) x (n) = w (n)+ (3.1) where, = e (n) x (n) the right to express filter update vector iterative adjust the volume. In order to achieve the purpose of fast convergence, we must choose the appropriate value of variable step size, a possible strategy is as much as possible to reduce the instantaneous squared error, which uses the instantaneous squared error as the mean square error MSE of the simple estimate, which is the basic LMS algorithm思想.
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Size: 3072 |
Author: 闫丰 |
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Description: ％％ Active Noise Control Using a Filtered-X LMS FIR Adaptive Filter
％ This demonstration illustrates the application of adaptive filters to the
％ attenuation of acoustic noise via active noise control.
- Active Noise Control Using a Filtered-X LMS FIR Adaptive Filter This demonstration illustrates the application of adaptive filters to the attenuation of acoustic noise via active noise control.
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Size: 3072 |
Author: 呙涛 |
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Description: Active Noise Control Using a Filtered-X LMS FIR Adaptive Filter.
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Size: 817152 |
Author: chrispin |
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Description: FIR_A=[1 1 2]
FIR_B=[2 1 1]
function [w_out mse_out ref_out] = LMS(FIR_A,FIR_B,1,wave=square)
[w mse ref res iter] = LMS(FIR_A,FIR_B,L,wave)
LMS filter to solve the system identification problem represented below:
---------- ----------
------- FILTER A -----out_A----- FILTER X ---out--
| ---------- ---------- |
in | |
----| |+
| ---------- - -----
------- FILTER B -----out_B-------------------- SUM ---error---
---------- -----
FILTER_X is unknown and to be derived. This problem is called "filter matching"
and is encountered when one needs to augment a certain filter (A)
in order to match the behavior of a reference filter (B).-FIR_A=[1 1 2]
FIR_B=[2 1 1]
function [w_out mse_out ref_out] = LMS(FIR_A,FIR_B,1,wave=square)
[w mse ref res iter] = LMS(FIR_A,FIR_B,L,wave)
LMS filter to solve the system identification problem represented below:
---------- ----------
------- FILTER A -----out_A----- FILTER X ---out--
| ---------- ---------- |
in | |
----| |+
| ---------- - -----
------- FILTER B -----out_B-------------------- SUM ---error---
---------- -----
FILTER_X is unknown and to be derived. This problem is called "filter matching"
and is encountered when one needs to augment a certain filter (A)
in order to match the behavior of a reference filter (B).
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Size: 2048 |
Author: dasu |
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Description: 假设每个延时单元延时10ms.被传输的基带信号x(t)是一个0，1交替变换的矩形二进制脉冲序列，脉宽为10ms，并假设x(t)通过一个稳定的散射信道后才到达均衡器，成为2径信号，这两路信号幅度相等 ，相隔15ms。用MATLAB实现一个2级LMS均衡器-equalizer
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Size: 198656 |
Author: 赵俊霖 |
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Description: 这写都是我在做-x LMS噪声除噪中的一些源代码，最后的代码也有，请大家自己看-It was all my doing-x LMS noise than noise in some of the source code, the final code also, please look at their own
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Size: 10240 |
Author: 傅飞 |
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Description: 功能描述：测试LMS与RLS算法，比较两种算法的收敛特性
文件名：LMS_RLS_sim.m
测试用例：
x(n)+a1*x(n-1)+a2*x(n-2)=e(n)，a1=-1.6,a2=0.81,e(n)为高斯白噪声
文件输出：系数a1的值
调用函数：function [A] = LMS_Algo(M,N,mu,xn)
被调用：无
作者：mingcheng
编写时间：2009-10-13
修改时间：2009-10-13
版本：V1.0 - Function Description: Test LMS and RLS algorithm, the convergence characteristics were compared file name: LMS_RLS_sim.m test case: x (n)+ a1* x (n-1)+ a2* x (n-2) = e (n), a1 =- 1.6, a2 = 0.81, e (n) is Gaussian white noise file output: the value of coefficient a1 call the function: function [A] = LMS_Algo (M, N, mu, xn) is called: No of: mingcheng write time :2009-10-13 modified :2009-10-13 version: V1.0
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Size: 1024 |
Author: 赵明诚 |
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Description: This derivation of the normalised least mean square algorithm is based on Farhang-
Boroujeny 1999, pp.172-175, and Diniz 1997, pp 150-3. To derive the NLMS algorithm
we consider the standard LMS recursion, for which we select a variable step size
parameter, μ(n). This parameter is selected so that the error value , e+(n), will be
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Size: 1024 |
Author: bahtiar |
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Description: 数字信号处理，LMS和RLS实例：给定正弦信号s(n)，现在我们获得得是受影响的数据x(n)=s(n)+v(n) ， v(n)为方差1.25的告示白噪声信号，请设计一个滤波器，使其输出与s(n)的均方误差最小，并给出用LMS和RLS算法的自适应求解方法的MATLAB仿真。-Digital signal processing, LMS and RLS instance: Given a sinusoidal signal s (n), now we get the data have affected x (n) = s (n)+ v (n), v (n) 1.25 for the variance Notice the white noise signal, to design a filter so that the output and s (n) The minimum mean square error, and gives the algorithm with the LMS and RLS adaptive method for solving the MATLAB simulation.
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Size: 1024 |
Author: codeshare |
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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: 自适应滤波LMS算法实现有源噪声消除：Mtalab程序；FLMS算法-Application Program to Test Active Noise Controla 32-tap adaptive FIR filter is used to produce an anti-noise to cancel the primary noise. The adaptive algorithms used here are the filtered-x LMS (FXLMS) and normalized FXLMS algorithms
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Size: 1126400 |
Author: 阿狸 |
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Description: 使用matlab编写的一种优化的lms算法 xlms算法,比lms算法更精准-Lms algorithm using an optimized algorithm matlab prepared xlms more accurate than lms algorithm
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Size: 1024 |
Author: 孟琦 |
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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: 基于 FXLMS 算法的窄带主动噪声控制系统性能分析研究，统计最小均方（LMS）理论为分析基础，对基于滤波 - X 最小均方（Filtered - X LMS: FXLMS）算法的窄带 ANC 系统展开详尽深入的性能分析(Adaptive Active Control System of Vehicle Noise Design)
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Size: 2919424 |
Author: willhu
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Description: LMS最小二乘法，拟合曲线的基本原理：成对等精度地测得一组数据x,试找出一条最佳的拟合曲线。(LMS Least Squares, the basic principle of fitting the curve: pairs of precision to measure a set of data x, try to find a best fit curve.)
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Size: 9216 |
Author: 唐tang
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