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add round matlab very important

秩和检验的Matlab源码程序，计算两类变量所含显著差异的变量(Rank sum test of the Matlab source code program to calculate two types of variables contained in significant differences in the variables)

说明：  同步电机模型的MATLAB仿真。。本文针对同步电机中具有代表性的凸极机，在忽略了一部分对误差影响较小而使算法复杂度大大增加的因素（如谐波磁势等），对其内部电流、电压、磁通、磁链及转矩的相互关系进行了一系列定量分析，建立了简化的基于abc三相变量上的数学模型，并将其进行派克变换，转换成易于计算机控制的d/q坐标下的模型。(MATLAB simulation of synchronous machine model. . Synchronous motor in this paper, salient pole machine with a representative, in the neglected part of the error is less impact on the complexity of the algorithm greatly increase the leaving of factors (such as the harmonic magnetic potential, etc.), its internal current, voltage, magnetic flux, flux and torque relationship between a series of quantitative analysis, a simplified three-phase abc variables based on the mathematical model, and the Park transformation, into easy-to-computer control of the d/q coordinate model .)

线性卷积运算的基于MATLAB的仿真程序(Linear convolution operation of the simulation program based on MATLAB)

A Multiset implementation with predictable iteration order.

Fast search for Dirichlet process mixture models (This code implements the search algorithms (modulo a few minor changes) described in the Fast search for DPMMs paper at AI-Stats 2007. It should work out of the box with a reasonably recent version of matlab. Currently the code only contains the Dirichlet/Multinomial case, but the Gaussian case can be hacked in in about 5 minutes.)

基于机器视觉的简单圆柱形零件图像的直径测量，采用zernike矩亚像素边缘定位的方法实现。(Diameter measurement based on machine vision simple cylindrical part of the image, the method of moments subpixel edge location zernike implementation.)

二维边界元Matlab程序 附初始条件 运行结果 (2D Matlab BEM program)

说明：  在Matlab软件上实现Mersenne Twister随机数的生成，并对结果进行仿真。(In the Matlab software to achieve Mersenne Twister random number generation, and the results are to be simulated.)

PSO算法简单、易实现且参数较少，现已被应用于函数优化、神经网络训练、模糊系统控制以及其它遗传算法的应用领域(PSO algorithm is simple, easy to implement and less parameters, have been applied to function optimization, neural network training, fuzzy system control and other applications of genetic algorithms)