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Description: 1、文件夹中包含了经典功率谱估计和自适应均衡算法两个实验的所有程序。
2、R.m、LMS.m、LMSmain.m为自适应均衡算法的程序：
R.m用来计算输入信号的自相关矩阵及其特征值；
LMS.m为时域LMS算法，用统计的方法仿真得出不同信道参数和不同步长下的学习曲线； LMSmain.m为实验主程序，按照实验要求中的具体数据得到实验结果和曲线。
3、functionx.m、fzhouqitu.m、spectrum.m、bt.m、bart_lett.m、welch.m、SPECTRUMmain.m为经典谱估计的程序：
functionx.m产生需要进行谱估计的函数；
fzhouqitu.m用来计算信号周期图的函数；
spectrum.m是用周期图法进行谱估计的函数
bt.m是用BT图法进行谱估计的函数
bart_lett.m是用BARTLETT法进行谱估计的函数
welch.m是用WELCH法进行谱估计的函数
SPECTRUMmain.m是主程序，按照实验要求中的具体数据得到实验结果和曲线。 -a document folder contains the classic power spectrum estimation and adaptive equalization algorithm for the two experiments all the procedures. 2, R.m, LMS.m, LMSmain.m adaptive equalization algorithm for the procedure : R.m used to calculate the input signal and the correlation matrix eigenvalue; LMS.m too LMS algorithm, use statistical simulation method come to a different channel parameters and the synchronous learning curve; LMSmain.m main program for the experiment, according to the experimental requirements of the specific data and the curve of the experimental results. 3, functionx.m, fzhouqitu.m, spectrum.m. bt.m, bart_lett.m, welch.m. SPECTRUMmain.m classical spectrum estimation procedures : functionx.m have a need for spectrum estimation function; fzhouqitu.m used to calculate t
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Author: yidishui |
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Description: 1、文件夹中包含了经典功率谱估计和自适应均衡算法两个实验的所有程序。
2、R.m、LMS.m、LMSmain.m为自适应均衡算法的程序：
R.m用来计算输入信号的自相关矩阵及其特征值；
LMS.m为时域LMS算法，用统计的方法仿真得出不同信道参数和不同步长下的学习曲线； LMSmain.m为实验主程序，按照实验要求中的具体数据得到实验结果和曲线。
3、functionx.m、fzhouqitu.m、spectrum.m、bt.m、bart_lett.m、welch.m、SPECTRUMmain.m为经典谱估计的程序：
functionx.m产生需要进行谱估计的函数；
fzhouqitu.m用来计算信号周期图的函数；
spectrum.m是用周期图法进行谱估计的函数
bt.m是用BT图法进行谱估计的函数
bart_lett.m是用BARTLETT法进行谱估计的函数
welch.m是用WELCH法进行谱估计的函数
SPECTRUMmain.m是主程序，按照实验要求中的具体数据得到实验结果和曲线。 -a document folder contains the classic power spectrum estimation and adaptive equalization algorithm for the two experiments all the procedures. 2, R.m, LMS.m, LMSmain.m adaptive equalization algorithm for the procedure : R.m used to calculate the input signal and the correlation matrix eigenvalue; LMS.m too LMS algorithm, use statistical simulation method come to a different channel parameters and the synchronous learning curve; LMSmain.m main program for the experiment, according to the experimental requirements of the specific data and the curve of the experimental results. 3, functionx.m, fzhouqitu.m, spectrum.m. bt.m, bart_lett.m, welch.m. SPECTRUMmain.m classical spectrum estimation procedures : functionx.m have a need for spectrum estimation function; fzhouqitu.m used to calculate t
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Description: 最小均方（LMS）自适应算法就是一中已期望响应和滤波输出信号之间误差的均方值最小为准的，依据输入信号在迭代过程中估计梯度矢量，并更新权系数以达到最优的自适应迭代算法。LMS算法是一种梯度最速下降方法，其显著的特点是它的简单性。这算法不需要计算相应的相关函数，也不需要进行矩阵运算。-Minimum mean-square (LMS) adaptive algorithm is that one has to respond to the expectations and filtering the output signal of the mean square error between the value of the minimum-based, based on the input signal is estimated in the iterative process of gradient vector, and to update the weights in order to achieve the most Adaptive iterative algorithm gifted. LMS algorithm is a steepest descent gradient method, and its significant features is its simplicity. This algorithm does not require calculation of the corresponding correlation function, nor the need for matrix calculation.
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Author: 闫丰 |
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Description: 很好的介绍了自适应滤波器的一些相关算法，lms算法，rls算法-Very good introduction to some of the adaptive filter correlation algorithm, lms algorithm, rls algorithm and so on
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Size: 7168 |
Author: 王波 |
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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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Author: 王凤 |
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Description: 给出一种LMS算法，即最小均方算法，采用一种特殊的梯度估值，它的显著特点是它的简单性，不需要计算相关函数，也不需要矩阵求逆运算。-Gives a LMS algorithm, least mean square algorithm, using a special gradient estimates, and its distinctive feature is its simplicity, need not calculate the correlation function, does not require matrix inversion.
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Size: 51200 |
Author: lvxiaoli |
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Description: 在一种变步长算法基础上, 从语音信号相关性的角度出发, 提出了一种新的去相关变步长LMS 算法( DCL—NLMS) 。该算
法结构简单, 收敛速度快, 稳态失调小, 计算量与NLMS 算法相当。仿真结果表明, 该算法在处理强相关性信号时, 不仅收敛速度明
显快于其余算法, 而且稳态失调特性也有很大优势-Based on the variable step NLMS arithmetic, this paper proposes a new uncorrelated variable step arithmetic( DCL—
NLMS) by employing auto—correlation of the input speech signal.The proposed algorithm has simple structure, fast convergence
speed, but small stability maladjustment with the same computational complexity as NLMS.Simulation results show not only its
convergence is faster than that of the others, but also the stable maladjustment of it is better too.
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Size: 444416 |
Author: 李丽 |
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Description: 本文对变步长(VSS) 自适应滤波算法进行了分析,针对输入信号高度相关时算法收敛速度下降导致性能下降
的问题,提出了一种解相关变步长归一化LMS 自适应算法,引入相关性原理,使得算法保持良好的收敛性能。计算机仿真
结果与理论分析相一致-In this paper , we discuss and analyze the variable step2size adaptive algorithm(VSSLMS) ,in which the descent
of it s convergence speed leads to the reduction of performance when input signals are heavily correlated . As a result , we
propose an uncorrelated variable step2size normalized LMS adaptive algorithm. Due to the principle of correlation ,the algo2
rithm can achieve good performance. The simulations agree to the theoretical analysis.
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Author: 李丽 |
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Description: 人工神经网络ANN程序，带图形界面，可以选用不同学习规则(Hebb, Delta, LMS, Conceptron, Correlation)和不同的转移函数-ANN artificial neural network program with a graphical interface, you can use different learning rules (Hebb, Delta, LMS, Conceptron, Correlation) and different transfer functions
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Size: 8192 |
Author: zoe |
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Description: 产生一个随机信号和两个不同频率但频率间隔很小的正弦信号，要求对两信号之和进行如下分析：
（1） 求该随机信号的自相关性系数、自相关函数，画出对应的图形；
（2） 利用不同的参数建模方法求出两个随机信号的功率谱；
（3） 利用极大似然估计、递推最小二乘法等常用的参数估计方法估计所建模型，包括AR模型、MA模型和ARMA模型的的参数，阶次自定；并与Matlab工具箱里的一些建模函数的运算结果进行比较；
（4） 利用陷波滤波和MUSIC滤波方法对该信号的频谱进行估计；
（5） 利用Wiener滤波、LMS滤波对该含噪声的正弦信号进行去噪声处理；
（6） 假设该信号是一个飞行器的某个方向的线位移信号，可否利用Kalman滤波对该信号进行滤波？
（7） 利用高阶谱理论对该信号进行谱估计和相应的AR模型、MA模型和ARMA模型估计；
（8） 利用小波变换（变换函数可以直接用Matlab里的函数）对该信号进行去噪声处理，并和前面的去噪声方法进行比较。
-Produce a random signal and two different frequency but frequency interval small sine signals, required to make the following analysis of two signals:
(1) for the random signal from the correlation coefficients, the autocorrelation function and draw the corresponding graphics
(2) make use of different parameters modeling method for out two random signal power spectrum
(3) using maximum likelihood estimation, recursive least square method and common parameters estimation method estimates that the model, including AR model, MA model and the parameters of the ARMA model we, order time decided oneself With Matlab toolbox and some of the modeling function is used compared the
(4) use trapped wave filter and MUSIC of the signal filter method of spectrum to estimate
(5) use Wiener filtering, LMS filtering on the sinusoidal signal with noise to noise treatment
(6) assumes the signal is a vehicle of a certain direction of displacement signal line, can you use Kalman filtering on the
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Author: 李思青 |
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Description: 利用相关的瞬时值通过在工作过程中的逐步调整参数逼近信号的统计特性，实现最优滤波。由此，我们得到一种最常用的算法——最小均方算法，简称LMS算法。-By using the instantaneous value of the correlation operation of the progressive approximation of the statistical properties of the signal to adjust the parameters to achieve optimal filtering. Thus, we get one of the most commonly used algorithms- the minimum mean square algorithm, called LMS algorithm.
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Size: 1024 |
Author: 唐心 |
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Description: LMS测相算法仿真（直线阵），通过算法产生阵列流型矩阵并进行相关分析,加噪-LMS algorithm simulation test phase (linear array), generated by an algorithm array manifold matrix and correlation analysis, noise
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Size: 1024 |
Author: 徐爽 |
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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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Author: emman |
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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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Description: lms滤波mcc自适应滤波最大相关熵准则自适应滤波两个滤波器(lms LMS filtering MCC adaptive filtering maximum correlation entropy criterion adaptive filtering two filters)
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Size: 2048 |
Author: 舟雨眠 |
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