curve-fitting-toolbox

说明：  Matlab中的fft分析含噪声的信号，其中有直接FFT，时域同步平均后FFT，频域同步平均后FFT(Matlab s fft analysis with noise signals, which have a direct FFT, time-domain synchronous average, after the FFT, frequency domain synchronization for after averaging FFT)

成功的模拟DBPSK误码率，而且精度特别高，值得学习的人好好借鉴一下(BER simulation of DBPSK successful, but accuracy is particularly high, it is a good reference to learn about people)

there is a code for tsai wu failure teory.

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.)

jugal beam analysis m file in matlab

FEM solution for microstrip

计盒分形维数计算的matlab源代码编程实现过程(Function [Fdgen NoisfreFD ]=fractaldim(y,Q,M) __________________________________________________________________________ Usage: Computes fractal dimension by box counting(BC) method. NOTE: Running the code may take a little time, because it calculates dimensions for all embedding dimensions up to M. This code is based on an algorithm that constructs a box for the first observation and for other observations test which it belongs to previous box(s). If the observation belongs to one of the existent boxes then increases number of points in the relevant box,otherwise makes a new box. the algorithm of this code uses only rounding down and it does not need binary coding, sorting and so on. It uses only transformation data to [0,2^32-1] and finding valid boxes based on Leibovich and Toth(1989). Inputs: y is a vector time series. q stands for generalized dimension q=1 entropy dimension,q=2 correlation dimension and so on. q=0 is box counting dimen)

Line Modulation in matlab

add kml file to matlan

object oriented handling example