FXLMS_MA_differ_mu

BEC_decode ldpc and BEC channel

function [xphi, phi, xpsi, psi] = Plot_Multiwavelet(H, r, m, n_refine) Compute the point values of the scaling function and wavelet(s) at all points of the form n*dx which fall inside the support of phi.

2007电工杯数学建模A题机组组合的LINGO程序源代码 (The Electrotechnical Cup 2007 A question of procedure)

说明：  matlab 实现一维Ｓ变换 逆变换，S变换由加拿大地球物理专家Stockewell 首先提出具有独特的优点(matlab realize one-dimensional inverse transform S transform, S transform geophysical experts from Canada Stockewell first has a unique advantage of)

本程序使用matlab来解决二位热传导问题第二类初边值条件下的数值求解和可视化表示。(This program uses Matlab to solve the two heat conduction problems of the numerical solution of initial and boundary conditions and visualization.)

我所见过的最实用的matlab编程实用教程，内容涉及到数据处理的方方面面，还有生成各种图表的方法，都有例程(I' ve seen the most useful and practical tutorial matlab programming, content related to all aspects of data processing, as well as the method generates a variety of charts, there is routine)

Audio Feature Extraction routine. this routine extracts the attack time of an audio source, by extracting the timing (sample) of the highest peak. The archive inclues a routine to extract the fft and display it. Requires Voicebox for matlab.

数学建模的程序，用于绘制多元因素的图，2011年建模的程序(Mathematical modeling procedures, multiple factors used to draw maps, 2011 modeling procedures)

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

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