MH

matlab并行计算 很好很系统的讲诉并行计算在matlab是如何操作的(matlab parallel computing good system v. matlab parallel computing is how to operate)

改程序对一个信号分别进行BPSK、PCM、PM三种处理。(Change procedures for a signal, respectively, BPSK, PCM, PM, three kinds of processing.)

基于牛顿拉夫逊发 用于电力系统的潮流计算 适合各种节点 (Made based on the Newton Raphson load flow calculation for electric power systems for a variety of nodes)

该程序是基于matlab的LMS自适应滤波器程序（matlab)(LMS program (matlab))

利用TV算法实现两幅图像的融合，融合规则采用最大值选取规则，测量矩阵采用傅里叶星型抽样模型(TV algorithm using two images of fusion, fusion rule with the maximum selection rule, measurement matrix Fourier sampling model star)

说明：  一个多分类支持向量机的文件，可直接运行，并且可以多数据进行多重分类，十分好用(A multi-class support vector machine files can be directly run, and can make multiple multiple data classification, is very easy to use)

本程序利用显式差分格式，将波动方程离散化，进而求出波动方程的数值解，不断地细化空间和时间步长，可以让数值解逼近理论解。(The program with the implicit difference method, the discrete wave equation, the numerical solution is obtained, continue to refine the long walk the numerical solution can approach the theoretical solution.)

产生随机变量，服从generalized高斯分布的随机变量的代码(GGD)

统计与自适应信号处理，对智能天线的 学习有帮助(Statistical and adaptive signal processing, the study of smart antenna help)

The shuffled complex evolution with principal components analysis–University of California at Irvine (SP-UCI) method is a global optimization algorithm designed for high-dimensional and complex problems. It is based on the Shuffled Complex Evolution (SCE-UA) Method (by Dr. Qingyun Duan et al.), but solves a serious problem in searching over high-dimensional spaces," population degeneration". The population degeneration problem refers to the phenomenon that, when searching over the highdimensional parameter spaces, the population of the searching particles is very likely to collapse into a subspace of the parameter space, therefore losing the capability of exploring the entire parameter space. In addition, the SP-UCI method also combines the strength of shuffled complex, the Nelder-Mead simplex, and mutinormal resampling to achieve efficient and effective high-dimensional optimization.