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最小均方自适应滤波噪声抑制，MATLAB编程，绝对好用。-Least mean square adaptive filter noise suppression, MATLAB programming, the absolute ease of use.
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thes source codes implements Adaptive filters.
Adaptive mean filter, Contra Harmonic Mean filter, Harmonic Mean Filter, Geometric Mean Filter and Spatial Filtering.
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LMS: Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual
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LMS: Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual
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Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual sign
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采用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波 图像的的自适应魏纳滤波 运用5种不同的梯度增强法进行图像锐化 图像的高通滤波和掩模处理
-Adaptive Wiener filtering in MATLAB functions filter2 the image noise by the mean filter images using five different gradient Enhancement Act for image sharpening the i
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LMS自适应滤波器是使滤波器的输出信号与期望响应之间的误差的均方值为最小，因此称为最小均方（LMS）自适应滤波器。-LMS adaptive filter so that the filter output signal and the expected response the error between the minimum mean square value, so called minimum mean square (LMS) adaptive filter.
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least mean square LMS自适应滤波器算法matlab代码，对自适应设计有很好的参考价值-least mean square LMS adaptive filter algorithm matlab code, a good reference value of adaptive design
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LMS algorithm for adaptive filter least mean squares-LMS algorithm for adaptive filter least mean squares
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格-梯型结构的LMS算法，按照最小均方准则，设计出阶数和时间分别递推的自适应滤波器，可以进行梯型滤波，又具有格型预测前后节独立、收敛快速的优点，但计算量大。-Grid- LMS algorithm in a ladder-type structure, in accordance with the minimum mean square criterion, design order and time adaptive recursive filter, ladder filter, and l
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1、产生信号，两个实正弦信号的叠加，幅度分别为2、4、1、3；混入均值为0、方差为1的白噪声。采用自适应滤波器对其进行去噪。
2.产生信号，为两个实正弦信号的叠加，其幅度均为4，混入均值为0、方差为1的白噪声。采用有限脉冲响应法设计一个维纳滤波器估计信号 ，并求最小均方误差。
3.产生高斯分布的白噪声w(n)，自行给定一个5阶AR模型，让该白噪声通过这个AR模型，得到输出信号x(n），再估计x(n）的AR模型数，比较估计的结果和原来给定的AR模型的参数。-A signal to be g
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传统的宽带信号中抑制正弦干扰的方法是采用陷波器(notch filter),为此需要精确知道干扰正弦的频率.然而当干扰正弦频率是缓慢变化时,且选频率特性要求十分尖锐时,则最好采用自适应噪声抵消的方法.下图是用一个二阶FIR的LMS自适应滤波器消除正弦干扰的一个方案。该代码实现了以下功能：
1) 借助MATLAB画出了滤波器的误差性能曲面和误差性能曲面的等值曲线；
2） 给出了最陡下降法以及LMS算法的计算公式；
3） 用MATLAB产生了方差为0.05, 均值为0白噪音S(n)，并
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自适应最小均方误差滤波器的推导,正则方程,均方误差性能函数的几何意义,梯度法,LMS算法-Derivation of Adaptive Minimum Mean Square Error Filter, Canonical Equation, Geometric Meaning of Mean Square Error Performance Function, Gradient Method, LMS Algorithm
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MATLAB codes for adaptive filtering using least mean square, nominal LMS and Wiener filter using forward linear prediction and backward linear prediction.
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A least mean square updating process is used for a parallel structure of a filter where the unknown filter and the adaptive one are arranged in Parallel structure.
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