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用 windows 管道与 MATLAB 建立连接(bridging MATLAB with Windows pipe)

这是一个人关于，MATLAB转化为VC++能使用的，.dll文件的方法，很实用(This is a man on, MATLAB into VC++ can use,. Dll file method, it is practical)

说明：  一个随机数产生器(mt19937) 计算物理中Monte Carlo方法中的基本条件 周期为2^19937(a random number generator (mt19937) Computational Physics Monte Carlo methods of the basic conditions for the cycle 2 ^ 19937)

load flow in redial distribution network

本文是一片关于FFT的Matlab仿真分析，很具体，配有大量清晰的仿真图片~~有利于FFT初学者的学习(This article is one of the Matlab simulation on the FFT analysis, very specific, clear配有大量simulation picture ~ ~ is conducive to learning for beginners FFT)

混沌时间序列Matlab源程序,包含时间序列的时间延迟计算，关联积分计算，相空间重构，时间序列分解，Heaviside函数的计算，延迟时间和时间窗口计算，混沌吸引子关联维计算，重构相空间进行K_L变换，混沌吸引子关联维计算，Hurst指数分析，关联维和Kolmogorov熵计算，FFT计算序列平均周期，最大lyapunov指数计算，利用互信息法求时间延迟，混沌和噪声识别的源程序。(Matlab chaotic time series source, time includes the time series of delay calculation, correlation integral calculation, phase space reconstruction, time series decomposition, calculated Heaviside function, the delay time and the time window calculated correlation dimension of chaotic attractors calculated reconstruction phase space K_L transform computing correlation dimension of chaotic attractors, Hurst exponent analysis, correlation dimension and Kolmogorov entropy calculation, FFT calculation sequence averaging period, maximum lyapunov index, mutual information method the time delay, chaos and noise source identification.)

说明：  MATLAB数学实验之数据可视化，其中包含课件以及习题和答案，文档中附有源程序(MATLAB Mathematical experiments data visualization, which includes courseware, as well as exercises and answers, the document bearing source)

一种matlab与VC++混合编程的方法。供初学者参考学习。可以与MATLAB通讯。(One kind of matlab and VC++ mixed programming method. Reference for beginners to learn. You can communicate with MATLAB.)

深度典型相关性分析，在线性的CCA上增加了深度网络，以此来学习新特征并提高多模态数据之间相关性( Deep cca ,the deep network is added in linear CCA to learning description that the correlation is better between multi-modal datas)

matlab中研究非线性动力学现象程序，倍周期分岔 ，(matlab program in the study of nonlinear dynamical phenomena)