-TMF5

小波谱分析mallat算法经典程序，对原始信号进行低高频的信号分解以及重构，并且进行对比分析。(Algorithm for spectral analysis mallat small classic procedure, the original signal with low frequency signal decomposition and reconstruction, and compared.)

沿著軌道運動的紅點，画一个红点让其沿着所画图形开始运动(Along the orbital motion of the red dot)

是国外经典matlab教材的翻译本，内容详实，有大量例题，对于要进行数学建模的人大有裨益，所有程序都是经过验证的。(Is the translation of classic matlab materials abroad this informative, a large number of examples are proven.)

power system analysis- load flow analysis ppt

BCS signal and BCS signal

克里金法假定采样点之间的距离或方向可以反映可用于说明表面变化的空间相关性。克里金法工 具可将数学函数与指定数量的点或指定半径内的所有点进行拟合以确定每个位置的输出值。克里金法是一个多步过程；它包括数据的探索性统计分析、变异函数建模 和创建表面，还包括研究方差表面。当您了解数据中存在空间相关距离或方向偏差后，便会认为克里金法是最适合的方法。该方法通常用在土壤科学和地质中。(This function completes what I have started with the functions variogram and variogramfit. It is not intended to be a highly optimized code for performing kriging but may have educational value. Note that for using kriging, you should download the latest version of variogramfit. kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct that contains all necessary information on the variogram. vstruct is the forth output argument of the function variogramfit. This is a rudimentary, but easy to use function to perform an ordinary kriging interpolation. I call it rudimentary since it always includes ALL observations to estimate values at unsampled locations. This may not be necessary when sample locations are not within the autocorrelation range but would require something like a k nearest neighbor search algorithm or something similar. Thus, the alg)

说明：  采用身高和体重数据作为特征，分别假设二者相关或不相关，在正态分布假设下估计概率密度，建立最小错误率Bayes分类器，写出得到的决策规则，将该分类器应用到训练/测试样本(Bayes classifier is the most classic! Minimize the classification error. With the matlab language, with examples)

MATLAB程序。无线传感器节点的定位。(Location of a wireless sensor node)

模糊算法PID控制器参数再线自动整定MATLAB程序(Fuzzy PID controller then automatically tuning MATLAB)

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)