CrossValidation

利用MATLAB实现雷达对抗技术中单脉冲全向比幅法测向的仿真(Radar simulation using MATLAB confrontation art amplitude comparison monopulse omnidirectional measured direction)

MATLAB 7和Simulink英文简介(Introducing the MATLAB & Simulink)

Fibonacci Numbers, the Golden section and the Golden String

这个程序实现了基于OFDM系统的信道估计DFT算法的实现(This application implements the DFT channel estimation algorithm based on OFDM system implementation )

基于免疫禁忌混合算法的多目标最优潮流计算[PDF].(Multi-objective Optimal Power Flow Calculation Based on Immune/Tabu Search Hybrid Algorithm)

说明：  老外的电机学教材，最为重要的是有相应的MAtlab代码，每一章都有，十分详细，学习电机的必备工具书，文件是在太大，此处上传chap3的源码(Foreigners motor learning materials, the most important thing is there is a corresponding MAtlab code, each chapter has a very detailed study of the motor necessary books, documents are too large to upload here chap3 source)

对图形图像进行处理，使图形图像配准，更为准确(The graphic image processing, the graphics image registration, more accurate)

基于AGRAWAL, G. P. (2001). Nonlinear Fiber Optics (3rd ed.)中求解分布傅里叶变换解非线性薛定谔方程(Based AGRAWAL, GP (2001). Nonlinear Fiber Optics (3rd ed.) The Fourier transform of the distribution solution for solving the nonlinear Schrodinger equation)

对测井数据进行绘图显示计算相关的一些参数，比如孔隙度，渗透率，可动油含量等(The logging data are drawing related to calculate some parameters, such as porosity, permeability, movable oil content, etc)

分析了支持向量回归机在能源需求预测中的优势,确定了输入向量集合和输出向量集合,建立了基于Matlab技术的SVR能源需求预测模型.对我国1985-2008年能源需求相关数据进行模拟与仿真,并对中国2010年和2020年能源需求量进行预测.研究结果表明:一是中国未来对能源的需求量逐渐增加,从2010年的330400万吨标准煤上升到2020年418320万吨标准煤,年均增长率为2.39％；二是在解决我国能源系统小样本.非线性及高维模式识别问题中SVR比BP神经网络等方法有更高的预测精度.(Support vector regression analyzes advantage in energy demand forecast to determine the set of input vectors and output vector set, established SVR energy demand forecasting model based on Matlab technology. Energy demand for our 1985-2008 related data modeling and simulation , and China in 2010 and 2020 energy demand forecast results show that: First, China s increasing demand for energy in the future, from 3.304 billion tons of standard coal in 2010 rose to 4.1832 billion tons of standard coal in 2020, average annual growth rate of 2.39 second is to solve our energy system small sample nonlinear and high dimensional pattern recognition problem SVR higher prediction accuracy than the BP neural network method.)