convolution

basic segmentation

说明：  用于特征选择，尤其是在基因微阵列数据中，利用随机思想(feature selection)

matlab的电子教案，希望对学习数学建模的人有所帮助，尤其是学习matlab(matlab electronic lesson plans, I hope to learn mathematical modeling help people, especially the learning matlab)

感应双馈发电机系统的仿真，大学数值分析算法，实现典型相关分析，用于信号特征提取、信号消噪，包括AHP，因子分析，回归分析，聚类分析，包含位置式PID算法、积分分离式PID，时间序列数据分析中的梅林变换工具，使用混沌与分形分析的例程。( Simulation of doubly fed induction generator system, University of numerical analysis algorithms, Achieve canonical correlation analysis, For feature extraction, signal de-noising, Including AHP, factor analysis, regression analysis, cluster analysis, It contains positional PID algorithm, integral separate PID, Time series data analysis Mellin transform tool, Use Chaos and fractal analysis routines.)

说明：  用MATLAB产生简单三角信号及高斯信号(Generating simple triangle signal and Gaussian signal by MATLAB)

边缘检测Canny法的源程序,网上好像有人需要，所以就放上来了(Canny edge detection method of the source, the Internet seems to be any need, therefore Fangshanglai!)

网上到处都是一维k均值聚类，而这是一个二维k均值聚类的代码，老外写的，说明较详细，稍加修改就可以融入自己的项目中。(Everywhere online one-dimensional k-means clustering, which is a two-dimensional k-means clustering code, written by foreigners, the more detailed, slight modifications can be integrated into the project.)

PSO electric load dispatch

otsu算法的实现，自适应最佳阈值图像分割方法，最大类间方差算法(otsu algorithm, adaptive optimal threshold image segmentation method, Otsu Algorithm)

脱机手写体识别Matlab源程序 包括特征提取、bayes分类器、K近邻分类及最近邻分类。 TestScriptRecognition.m：测试代码 ScriptFeaExtract.m ：特征提取 KNearestEstimate.m ：K近邻估计 NearestEstimate.m : 最近邻估计 BayesTrain.m ：训练bayes分类器 Bayes.m ：测试bayes分类器 CrossValidate.m ：m交叉验证 (Offline Handwriting Recognition Matlab source including feature extraction, bayes classifier, K-nearest neighbor and nearest neighbor classifier. TestScriptRecognition.m : ScriptFeaExtract.m test code : Feature Extraction KNearestEstimate.m : K-nearest neighbor is estimated NearestEstimate.m : nearest neighbor is estimated BayesTrain.m : Training bayes classifier Bayes.m : Test bayes classifier CrossValidate.m : m cross-certification)