addnoisenew

this mfile is riten in matlab and is related to mirtoolbox

implementing dot product in matlab and ccs

本程序可以用于自然最近邻的搜索,这里以500个点威力(This procedure can be used to naturally nearest neighbor search)

Wheeled Mobile Robot Control Using Sliding Modes and Neural Networks

说明：  分形维数计算方法，使用cc法计算分形维数(Calculation method of fractal dimension)

function [modes its]=ceemdan(x,Nstd,NR,MaxIter)% WARNING: for this code works it is necessary to include in the same%directoy the file emd.m developed by Rilling and Flandrin.%This file is available at %http://perso.ens-lyon.fr/patrick.flandrin/emd.html%We use the default stopping criterion.%We use the last modification: 3.2007% % This version was run on Matlab 7.10.0 (R2010a)%----------------------------------------------------------------------% INPUTs% x: signal to decompose% Nstd: noise standard deviation% NR: number of realizations% MaxIter: maximum number of sifting iterations allowed.%% OUTPUTs% modes: contain the obtained modes in a matrix with the rows being the modes % its: contain the sifting iterations needed for each mode for each realization (one row for each realization)% -------------------------------------------------------------------------% Syntax%% modes=ceemdan(x,Nstd,NR,MaxIter)% [modes its]=ceemdan(x,Nstd,NR,MaxIter)%%--------------------------------------------------------------------------% This algorithm was presented at ICASSP 2011, Prague, Czech Republic% Plese, if you use this code in your work, please cite the paper where the% algorithm was first presented.

用split-step方法求解非线性薛定谔(NLS)方程的例子(This code solves the NLS equation with the split-step method)

matlab的svm神经网络文件，可用于风速预测，能够生成预测图(the rbf neural network matlab files, can be used for wind prediction, to generate prediction chart)

使用SVM分类器来预测乳腺癌病人的预后(特征选择；分类器构建)，评价模型时使用无被交叉验证，性能评价指标包括准确率，AUC，灵敏度，特异度。学会最基本的机器学习方法。可查看分发给大家的代码，以后遇到类似的问题，可用相似的思路和代码。(The SVM classifier was used to predict the prognosis of breast cancer patients (feature selection; classifier construction), and the model was used without cross-validation. Performance evaluation indicators included accuracy, AUC, sensitivity, and specificity. Learn the most basic machine learning methods. You can view the code distributed to everyone, and later encounter similar problems, similar ideas and code can be used.)

包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，毕业设计有用，各种资源分配算法实现，直线阵采用切比学夫加权控制主旁瓣比，粒子图像分割及匹配均为自行编制的子例程，供做算法研究人员参考。