ch_model_awgn

偏微分方程中关于有限差分的其中一个格式，向前差分格式(failed to translate)

基于灰色关联系数的模糊层次分析法。两种方法的综合运用，MATLAB编写，方便实用。(Correlation coefficient based on the fuzzy gray level analysis. Comprehensive use of the two methods, MATLAB prepared, convenient and practical.)

说明：  <<小波分析理论与matlab r2007实现>>教材中的源代码(&lt &lt Wavelet analysis theory and the realization of matlab r2007&gt &gt materials in the source code)

倒立摆的程序，很好的，大家可以看看，想要源程序的可以给我发e－mail(inverted pendulum procedures, good, we can see, wants the source can give me the e-mail)

Simple VaR Calculator provides: - Evaluation of return distribution of single asset or portfolio of assets - Volatility forecasts using moving average and exponential algorithm - Value at Risk of single asset or portfolio measurement using parametric and historical simulation. - Historical data can be obtained from simple text file or MS Excel using Matlab Excel Links. (Simple VaR Calculator provides:- Evaluation of return distribution of single asset or portfolio of assets- Volatility forecasts using moving average and exponential algorithm- Value at Risk of single asset or portfolio measurement using parametric and historical simulation .- Historical data can be obtained from simple text file or MS Excel using Matlab Excel Links.)

clustring(Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jö rg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.

matlab robotics 工具箱 机器人运动学计算资料(matlab robotics toolbox ：robot kinematics calculations)

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))

无线通信高斯白噪声信道各类调制方式，并且有星座图、调制曲线分析。(Wireless communications Gaussian white noise channel types of modulation methods, and has constellation, modulation curve analysis.)

基于OFDMA系统的多用户资源分配算法，论文配备MATLAB代码，入门的好教材。(— Orthogonal Frequency Division Multiple Access (OFDMA) basestations allow multiple users to transmit simultaneously on different subcarriers during the same symbol period. This paper considers basestation allocation of subcarriers and power to each user to maximize the sum of user data rates, subject to constraints on total power, bit error rate, and proportionality among user data rates. Previous allocation methods have been iterative nonlinear methods suitable for ofine optimization. In the special high subchannel SNR case, an iterative root-nding method has linear-time complexity in the number of users and N logN complexity in the number of subchannels. We propose a non-iterative method that is made possible by our relaxation of strict user rate proportionality constraints. Compared to the root-nding method, the proposed method waives the restriction of high subchannel SNR, has signicantly lower complexity, and in simulation, yields higher user data rates.)