Nearestneighbor

C实现应用程序框架的经典代码C application framework to achieve the classic code(C application framework to achieve the classic code)

EMD可以对振动噪声信号分解成多个本征模函数，可以进行时频分析(Time frequency analysis of EMD)

使用MATLAB编写的阴历和阳历转换程序(MATLAB prepared using the lunar calendar and Gregorian calendar conversion process)

基于MATLAB的turbo码的仿真研究(MATLAB-based Simulation of turbo codes)

用matlab实现三维情况下波的传播仿真，带有PEC边界.！(realize the propogation of electromagnetic under 3-D situation，with respect to the perfect electronic condutor.)

自己编写的一个随动系统的kalman滤波模型(I have written a servo system of the kalman filter model)

调制参数L=2的CPM调制解调，viterbi译码(Modulation parameters L = CPM modulation and demodulation 2, viterbi decoding)

In this project, the standard particles swarm optimization - named here (MyPSO) is simulated and tested by five well-known benchmark functions (Sphere, Ackley, Rastrigin, Rosenbrock and Schwefel p2.26). Then, a new mechanism- named here (Improved MyPSO) is introduced to improve the performance of the standard particles swarm optimization to achieve better results and avoid stagnation at bad results. This new mechanism is also tested by the same benchmark functions. The results of improved PSO are then compared with those of standard PSO and different published improved PSO (SPSO, PSO-XD, CPSO-S and PSO-P5).

经纬度生成KML文件,只需要输入经纬度的数据就能生成Google Earth 中使用的kml文件(Use your latitude and longtitude data to generate KML file)

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.)