Simulation-of-communication-system

说明：  这里面是一个灰色预测模型的程序，在预测方面有着很重要的作用(There is a gray prediction model program, over the forecast has a very important role)

MATLAB的数值积分工具箱，大家赶快下载来看看，一定不会让你失望(Numerical integration of MATLAB toolbox, we quickly downloaded to see that we will not let you down)

在matlab环境下实现对信号的小波变换；小波变换工具箱(wavelet toolbar)

系统辨识与自适应控制中----递推增广最小二乘参数估计算法(Augmented recursive least squares parameter estimation)

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))

ABAQUS UMAT 带剪切非线性HASHIN准则(ABAQUS UMAT)

说明：   用FFT计算信号x=cos3pit+2sin12pi*t 的频谱(Calculated using FFT signal x = cos3pit+2 sin12pi* t of the spectrum)

说明：  SOFC 重整器模型，包括参考文档，m.file,simulink模型(steam reformer for SOFC, include the world file,m file and simulink model)

数字预失真的LS算法，利用LS算法提取预失真参数(LS digital pre-distortion algorithm, using LS algorithm extracts the predistortion parameters)

This project provides matlab class for implementation of convolutional neural networks. This networks was developed by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot navigation and others (see references for more info). Because of some architectural features of convolutional networks, such as weight sharing it is imposible to implement it using Matlab Neural Network Toolbox without it s source modifications.