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说明：  这个源代码是小世界网络模型MATLAB仿真源代码。(small-world network )

基于Matlab/Simulink异步电动机的多用仿真模型研究(Multi-use simulation model based on Matlab/Simulink asynchronous motor)

This program simultes the BER performance with a single and multiple users

给出不同情况下单缝菲涅尔衍射的光强分布图和灰度图(The intensity distribution and grayscale map of single Ja Faye Nel diffraction under different conditions are given.)

说明：  差分进化算法(Differential Evolution Algorithm，DE)是一种高效的全局优化算法。它也是基于群体的启发式搜索算法，群中的每个个体对应一个解向量。差分进化算法的进化流程则与遗传算法非常类似，都包括变异、杂交和选择操作，但这些操作的具体定义与遗传算法有所不同(Differential evolution algorithm (DE) is an efficient global optimization algorithm. It is also a heuristic search algorithm based on population. Each individual in the group corresponds to a solution vector. The evolution process of differential evolution algorithm is very similar to genetic algorithm, including mutation, hybridization and selection operations, but the specific definition of these operations is different from that of genetic algorithm)

说明：  求三角对角方程组Tx=f的解，其中T=[1 1 0 0 0,1 2 1 0 0,1 1 3 1 0,0 0 1 4 1, 0 0 0 1 5],x =[x1,x2,x3，...x5],f=[3,8,15,24,29](Diagonal triangle demand equations Tx = f the solution, in which T = [1 1 0 0 0,1 2 1 0 0,1 1 3 1 0,0 0 1 4 1, 0 0 0 1 5], x = [ x1, x2, x3, ... x5], f = [3,8,15,24,29])

根据matlab的bvp4c函数的例子思想方法，求解特征值和特征函数，可以解变积分边界的问题，但是须给一个比较精确的初特征值。此为自编程序，已经标明注释。所附为bvp4c相关例子（此从matlab帮助文件节选）。(use the matlab solver bvp4c to obtain the both eigenvalues and eigenfunctions of BVPs, an exact initial eigenvalue is needed.)

基于多天线中的qpsk，bpsk的星座图的blast算法(Based on Multi-Antenna qpsk, bpsk the constellation diagram of the blast algorithm)

通过对语音信号的采集和AD50数模转换实现语音信号的处理，并通过C54试验台可以很好的实现语音再现。FIR数据滤波数据需要通过MATLAB来实现。(Through the acquisition and AD50 voice signal digital-analog conversion of voice signal processing, and through the C54 test bed can be a very good voice reproduction. FIR data filtering data to achieve through MATLAB.)

这个例子展示了如何应用偏最小二乘回归(plsr)和主成分回归(PCR),并讨论了两种方法的有效性。 本人亲测有效(This example shows how to apply Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR), and discusses the effectiveness of the two methods. PLSR and PCR are both methods to model a response variable when there are a large number of predictor variables, and those predictors are highly correlated or even collinear. Both methods construct new predictor variables, known as components,)