CPA

说明：  C++ SGP4 Satellite Library

这是几篇关于粒计算在故障诊断中的应用，对学习粒计算的应用很有帮助。(This is a few on Granular Computing in Fault Diagnosis of learning the application of granular computing helpful.)

求矩阵奇异分解svd算法，共计4种实现方式，(for SVD matrix decomposition algorithm, for a total of four kinds of achievement,)

说明：  Matlab；金融计算；var计算；cvar计算 [VaR&&CVaR] VAR，CVAR详细介绍，并附带各种方法计算，matlab程序实现，仿真结果图展示。 [Matlab和金融计算] Matlab实现金融计算，并附带蒙特卡洛实现。([VaR&&CVaR] Var, cvar are introduced in detail, as well as various methods of calculation, and matlab program calculation is included, and the results are displayed in graphs. [Matlab and financial calculations] Matlab implements financial calculations with Monte Carlo implementation.)

应用遗传算法做电网规划，把遗传算法应用于输电网络规划，提出了基于遗传算 法的输电网络规划模型，以新建线路的投资费用和系统年运 行费用之和最小为目标函数，建立了输电网络规划的数学模型。该模型还考虑了“N-1”事故检验，使得规划方案更加合理。以Garver-6节点系统为例进行优化规划(The application of genetic algorithms to do network planning)

homagrapy matrix to find the homagrapy matrix from two pictures, first you need to get a set of coorlation vector

说明：  非线性振动教程，学习振动的学者们必须看的东西。(Nonlinear vibration tutorial, learning vibration scholars must look at things.)

空间目标识别，采用PM算法，线性调频脉冲压缩的Matlab程序,多抽样率信号处理。( Space target recognition algorithm using PM, LFM pulse compression of the Matlab program, Multirate signal processing.)

经典矩阵运算问题求解(classic problem solving matrix operations)

包括基本的克里金(Kriging)插值法实现代码，仅实现基本方法部分，不包含扩展克里金方法( kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct that contains all necessary information on the variogram. vstruct is the forth output argument of the function variogramfit. This is a rudimentary, but easy to use function to perform a simple kriging interpolation. I call it rudimentary since it always includes ALL observations to estimate values at unsampled locations. This may not be necessary when sample locations are not within the autocorrelation range but would require something like a k nearest neighbor search algorithm or something similar. Thus, the algorithms works best for relatively small numbers of observations (100-500). For larger numbers of observations I recommend the use of GSTAT. Note that kriging fails if there are two or more observa)