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MATLAB 编程视频教程1 有声有色，更易明白，很好很强大(MATLAB Programming Video Tutorial 1 impressive, easier to understand, very good very strong)

这个仿真程序用于实现DS-CDMA通信系统仿真(The simulation program used to implement DS-CDMA Communication System Simulation)

人工智能，解决复杂问题。比如说，解决最优下料问题，解决最优规划问题等。(Artificial intelligence to solve complex problems. For example, the optimal solution under the material and solve optimal planning problems.)

无线传感器网络仿真例子文件loadAndPrintNet.m，networksDB.mat，oneRoundWSN.m.需要用到图论工具箱grtheory(Examples of wireless sensor network simulation file loadAndPrintNet.m, networksDB.mat, oneRoundWSN.m. Need to use graph theory toolbox grtheory)

MMSE和ZF信道均衡算法的matlab仿真程序，仿真信道有加性白高斯信道和瑞利衰落信道(MMSE and ZF channel equalization algorithm matlab simulation program)

本程序的性能已经超过其他算法，可实现对二维数据的聚类，是路径规划的实用方法，对HARQ系统的吞吐量分析，已调制信号计算其普相关密度，有CDF三角函数曲线/三维曲线图，采用波束成形技术的BER计算。(This program has exceeded the performance of other algorithms, Can realize the two-dimensional data clustering, Is a practical method of path planning, HARQ throughput analysis of the system, Modulated signals to calculate its density Pu-related, There CDF trigonometric curve/3D graphs, By applying the beam forming technology of BE.)

说明：  《MATLAB 7.0编程基础》第8章 （源码实例）主要讲解MATLAB图形用户界面设计("MATLAB 7.0 programming foundation" Chapter 8 (source code examples) mainly on MATLAB graphical user interface design)

说明：  本程序用matlab生成白噪声，并且基于一个离散线性随机系统的模型生成了y(k)和x(k)，绘制出了x(k|k-1)和x(k)的对比曲线，求出了提前一步预报的误差协方差阵的稳定值(the procedures used Matlab generate white noise, and on a discrete linear stochastic systems model generated y (k) and x (k), mapping out the x (k | k-1) and x (k) contrast curves, get a step ahead forecasting error covariance matrix of stable value)

这对于计算chut chutiye的摩擦力是一个很好的选择(this is a nice for calculating the friction of chut chutiye)

SVM方法的基本思想是：定义最优线性超平面，并把寻找最优线性超平面的算法归结为求解一个凸规划问题。进而基于Mercer核展开定理，通过非线性映射φ，把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），使在特征空间中可以应用线性学习机的方法解决样本空间中的高度非线性分类和回归等问题。svm 程序，即支持向量机的代码。(The basic idea of SVM method are: the definition of the optimal linear hyperplane, and the search algorithm for optimal linear hyperplane by solving a convex programming problem. Then based on Mercer nuclear expansion theorem, through a nonlinear mapping φ, the sample space is mapped to a high-dimensional and even infinite dimensional feature space (Hilbert space), so that in the feature space can be applied to solve the linear learning machine method, the sample space The highly nonlinear classification and regression problems. svm procedures that support vector machine code.)