gray-model

说明：  认知无线电频谱，打破僵局：信息理论.本文介绍了认知无线电的频谱检测技术和当前热门的合作检测技术(Cognitive radio spectrum, to break the deadlock: Information Theory. This article describes the spectrum sensing of cognitive radio technology and the cooperation of the current popular detection)

matlab下的GUI使用指导视频，让你快速掌握GUI的使用(GUI using matlab under the guidance of video, let you quickly master the use of GUI)

用matlab做方差分析的几个例程包括：单因素一元方差分析，双因素一元方差分析 非参数方差分析(ANOVA using matlab to do a few routines include: single-factor analysis of variance one yuan, two-factor analysis of variance one yuan non-parametric analysis of variance)

Particle filter for robot localization based on WiFi

本程序为用matlab实现的基本的GA算法(genetic algorithm)

说明：  TCP/AQM router design

Analysis beam propagration using Finite Difference Beam Propagation Method (Analysis beam propagration using Finite Difference Beam Propagation Method)

rossler分岔代码，很实用哦，稍微改下可用用哦(rosslerfenchangyouyongo )

说明：  无线传感器网络定位算法MDS-MAP仿真的matlab代码(Wireless sensor network localization algorithm MDS-MAP simulation matlab code)

逐步判别分析的主函数 用于分类 [sel,c,c0,re,P]=StepclaassV2(data,[50 50 50],data,10) 输入变量 x为训练集.每行为一个样本，每列为一个变量. Class_x,为训练集的分类情况，一行，为各类样本数量,例如[5 6 9] Test为待分类样本. 输出变量：sel为选择的变量序号，c,c0为拟合出的判别函数.re为对Test的判别结果,P为其后验概率. author 王新 2012.4.8 例子Sample: load fisheriris x=meas Class_x=[50,50,50] Test=meas(1:5,:) Fa1=4 [sel,c,c0,re,P]=StepclaassV2(x,Class_x,Test,Fa1)( Stepwise discriminant analysis for the classification of the main function [sel, c, c0, re, P] = StepclaassV2 (data, [50 50 50], data, 10) input variables x as the training set, each conduct a sample of each as a variable. Class_x, the classification for the training set, a row, the number of samples for all types, for example, [5 6 9] Test sample to be classified. output variable: sel for the selected variables serial number, c, c0 is fitted discriminant function. re discrimination results for the Test, P its posterior probability. author Wang 2012.4.8 examples Sample: load fisheriris x = meas Class_x = [50,50,50] Test = meas (1:5, :) Fa1 = 4 [sel, c, c0, re, P] = StepclaassV2 (x, Class_x, Test, Fa1))