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Description: 包含用lms、mse、perceptron准则函数的二类分类器
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Size: 1875872 |
Author: 黎维娟 |
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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。
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Size: 3285 |
Author: 闫丰 |
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Description: 利用LMS做自适应滤波，利用MSE算法做自适应均衡
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Size: 71070 |
Author: chenjianpei@126.com |
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Description: 线性MMSE均衡器的matlab源码,适应信道衰减不是很剧烈的情况-linear MMSE equalizer in Matlab source, adaptation channel attenuation is not very dramatic situation
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Size: 1024 |
Author: 张于 |
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Description: 基于LMS(最小均方误差算法)的自适应滤波 基于LMS(最小均方误差算法)的自适应滤波-based on the LMS (MMSE) algorithm based on the LMS adaptive filtering (minimum mean square error algorithm) the adaptive filtering based on the LMS (minimum mean square error algorithm) Adaptive Filter
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Size: 4096 |
Author: 林青 |
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Description: OFDM程序示例：采用64子信道，应用16QAM传输，每个子载波均传输相同的比特数4，不使用信道编码；使用迫零均衡和MMSE均衡，比较两者性能。-OFDM procedures Example: a 64-channel, application 16QAM transmission, each transmission subcarriers are the same number of bits 4, do not use channel coding the use of force balance and MMSE zero balance, compare the performance.
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Size: 2048 |
Author: 穆佳 |
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Description: 包含用lms、mse、perceptron准则函数的二类分类器-Includes use of lms, mse, perceptron criterion function of the second-class classifier
Platform: |
Size: 1875968 |
Author: 黎维娟 |
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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:
Platform: |
Size: 1024 |
Author: 张宗彬 |
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Description: 分析了相位噪声对正交频分复用(OFDM)系统的影响,发现相位噪声不仅产生
通用相角错误(CPE),而且还会产生载波间干扰(ICI),这都使OFDM系统的性能急剧恶
化,因此必须对CPE和ICI进行校正.文中给出了基于MSE准则的CPE校正方法,考虑到
ICI干扰主要来源于相邻信道,进而提出了基于LMS法则的自适应相邻信道干扰消除方
法.整个算法简单高效.仿真结果表明,所提出的算法大大改善了OFDM系统的性能-Analysis of phase noise on the orthogonal frequency division multiplexing (OFDM) system, the impact of phase noise was found not only from the general phase angle error (CPE), but also generate inter-carrier interference (ICI), which are so dramatically the performance of OFDM system worse, it is necessary CPE and ICI correction. This paper gives guidelines for CPE-based MSE correction methods, taking into account ICI mainly come from the adjacent channel interference, then presents a rule-based LMS adaptive adjacent channel interference cancellation method. the algorithm is simple and efficient. The simulation results show that the proposed algorithm greatly improved the performance of OFDM systems
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Size: 44032 |
Author: sunkai |
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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: 多种实现三组数据集（iris测试数据）分类的算法实现（LMS、MSE、HK等。-several methods(LMS,MSE,HK) to achieve classification of three data set（iris data set).
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Size: 13312 |
Author: yuchang |
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Description: 在分析最小均方自适应滤波器(LMSAF)均方误差(MSE)的收敛性时，文献常用统计自相关矩阵代替瞬时自相关矩阵以简化分析，由此得出的收敛条件比较粗糙。本程序指出：不相关高斯输入情况下，无需如上简化，可依据高斯阶矩因式分解定理得到确切的MSE收敛条件，相应的失调表式能更准确地预报失调-In the analysis of LMS adaptive filter (LMSAF) the mean square error (MSE) convergence, the literature commonly used statistical correlation matrix instead of the instantaneous correlation matrix to simplify the analysis, it follows that the convergence conditions are relatively rough. This procedure pointed out: not related to the Gaussian input case, no simplification above may be based on the Gaussian moment factoring theorem to the exact order of MSE convergence conditions, the corresponding offset table style to a more accurate prediction of disorder
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Size: 8192 |
Author: 王凤 |
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Description: 利用lms算法和rls算法，对通过给定系统h的随机信号进行自适应滤波，通过抽头w对系统进行逆辨识与辨识，同时产生MSE 即均方误差，来描述对信号恢复的效果。-Using lms algorithm and rls algorithms h through a given system adaptive filtering of random signals, the system through the tap w reverse identification and recognition, while the mean square error MSE generated to describe the effect on signal recovery.
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Size: 1024 |
Author: |
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Description: This paper compares performance of nite impulse
response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square(LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation results, in terms of steady-state
mean-square estimation error (MSE) and average bit-error rate (BER) metrics, are found for the frequency selective Rayleigh fading wireless channel experienced in a mobile ad hoc network where nodes are lognormally shadowed from each other. For the nonstationary channel models considered, RLS is always found to outperform LMS.
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Size: 844800 |
Author: almoudamer3 |
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Description: 针对数字通信系统中，由于码间串扰(ISI)和信道加性噪声的干扰，导致信号在接收端产生误码，设计了基于LMS算法的自适应均衡器(滤波器)。是一篇标准的毕业论文,有需要的朋友可以拿来做参考-Thesis for digital communications systems, crosstalk due to inter-symbol (ISI) and additive noise channel interference, leading to signals generated in the receiver error, design algorithm based on LMS adaptive equalizer (filter). Is a standard thesis, there is a need to make friends can be used as reference
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Size: 3072 |
Author: 张金锁 |
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Description: LMS算法的修改更好地衔接和使用两个步骤尺寸较小的MSE-Modification of the LMS algorithm is better convergence and the use of two steps smaller MSE
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Size: 37888 |
Author: 丫头 |
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Description: 最小均方误差算法的编程，简单有效，完整-MSE programming algorithm
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Size: 2048 |
Author: yanle |
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Description: 在MIMO信道中仿真，LS，LMS，RLS,，LMS算法的MSE表现(Simulation in MIMO channel, MSE performance of LS, LMS, RLS, LMS algorithm)
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Size: 3072 |
Author: ScorpioY |
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Description: 1、可用于改进变步长LMS算法文章
2、横坐标迭代次数纵坐标MSE
3、为了使结果更优，加入颜色字体字号改变，RGB
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Size: 183039 |
Author: xingfubushiqingge |
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