load_shedding - 副本

ma730磁性编码器的使用说明，数据手册(MA730 Magnetic Encoder Instructions, Data Manual)

说明：  金蝶KIS行政事业版V12.1 金蝶KIS旗舰版V4.0 金蝶KIS商贸版V6.1 金蝶KIS专业版V14.0 四合一注册机(Kingdee KIS Administration Version V12.1 Kingdee KIS Flagship Version V4.0 Kingdee KIS Business Version V6.1 Kingdee KIS Professional Version V14.0 Quadruple Register)

"Calcoolation" is a puzzle game that at first looks like the popular Sudoku. Just like in Sudoku, you have an N x N matrix, where the digits 1 through N must be placed in each column and in each row without repetition. The difference lies in the fact that there are blocks of cells, named "cages", where specific operations involving the numbers in the cells must satisfy the specific result displayed in the cage.

pic单片机定时器使用详解，介绍了定时器的功能，如何设置，以及使用时的注意事项。-pic explain the use of single-chip timer, the timer function of introduction, how to set up, and the use of Notes.

一种流形学习算法（很好用），主要是基于mtlab的程序，关于非线性离散系统辨识。( A fluid manifold learning algorithm (good use), Mainly based on the mtlab procedures, Nonlinear discrete system identification.)

说明：  利用IDL进行批量对遥感影像进行重命名，非常方便(Rename remote sensing images in batches)

说明：  PSO-BP代码，实现模型预测，这是最基础的模型可以进行修改优化(PSO-BP code to realize model prediction, which is the most basic model that can be modified and optimized)

用vc开发的 一个很好的坦克小游戏 ，，可以引用源码的-vc development with a good game of small tanks, and invoke the source.

C++ programs for students

常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化（ Infomax ）以及独 立成分分析（ ICA ）等。这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关， 一旦假设的概率密度与实际信号的密度函数相差甚远，分离性能将大大降低。本文提出采用 核函数密度估计的方法进行任意信号源的盲分离，并通过典型算例与几种盲分离算法进行了 性能比较，验证了方法的可行性。(The commonly used blind separation algorithms include two order statistics, higher order cumulants, and maximization of information Infomax) and independent component analysis (ICA). The conditions for obtaining the best performance of these methods are always related to the hypothesis of the probability density function of the source signal. The separation performance will be greatly reduced when the assumed probability density is very different from the density function of the actual signal. In this paper, a method of kernel function density estimation for blind separation of arbitrary signal sources is proposed. The performance of several blind separation algorithms is compared with some typical examples, and the feasibility of the method is verified.)