pingjiasuanfa

说明：  MATLAB有限元分析与应用，适用于matlab编程研究人员(Finite element analysis and application of MATLAB, matlab Programming for Researchers)

本书详细讲述了如何用MATLAB进行程序设计，如何编写清楚、高效的程序。书中强调了自上而下的程序设计方法、函数的使用、MATLAB内部工具的使用和数据结构，并指出了一些使用技巧和编程者常犯的错误。(This book describes in detail how to use MATLAB for programming, how to write clear and efficient procedures. The book emphasizes top-down program design, function, use, MATLAB uses internal tools and data structures, and pointed out some tips and common mistakes programmers.)

说明：  matlab中的关于微分方程实验方面的例子和程序(matlab in the example of experiments on differential equations and procedures)

ＣＡＰＭ模型的Matlab实现程序。用于计算最优证券组合的配置和权重。(CAPM model of Matlab procedures. Used to calculate the optimal portfolio configuration and weight.)

实现M=4,8 PSK星设图 M=4的PSK系统，分别取信噪比为odB、10dB和20dB，在星座图上观察接收端接收到的信号向量。(achieve M = 4,8 PSK-based map of M = 4 PSK system, the signal-to-noise were taken odB. 10dB and 20dB, in the constellation observation receiving termination received signal vector.)

喇叭天线的内部TE模式传播的电流电压分布图，可以手动输入参数，如扫描次数和时间，能够仿真出动态图形。好好研读软件，编程能力会迅速提高的~(Current and voltage distribution inside the TE mode horn antenna transmission, you can manually enter parameters such as scan frequency and duration, can simulate the dynamic graphics. Good reading software, programming capability will rapidly increase the ~)

利用台湾大学教授所开发的支持向量机工具箱做预测和分类算法(prediction using svm)

matlab code for video watermarking

复杂网络中有一些常见的基本模型，如随机图，WS小世界网络，NW小世界网络及BA无标度网络等，本程序从这些网络的产生机理，生成了这些复杂网络模型，并且分析了其基本性质如平均路径长度，聚类系数及度分布等。(Complex networks have some common basic model, such as random graph, WS small-world networks, NW small-world networks and scale-free BA network, the program generated from the mechanism of these networks to generate the complex network model and analysis its basic nature, such as the average path length, clustering coefficient and degree distribution.)

In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.