pcaForSVM

已知观测数据为A+W(n)，其中A为直流信号，W为复高斯噪声，服从CN（0， ），在统计N次下，计算X的统计平均值 ，求T(x)= 的pdf：p(T(x)|H0)、p(T(x)|H1)。 H0： W H1： A+W (Known observation data A+W (n), where A is the DC signal, W is the complex Gaussian noise, subject CN (0,), N times in the statistics, the calculation of the statistical mean of X, find T (x) = a pdf : p (T (x) | H0), p (T (x) | H1). H0: W H1: A+W)

Power flow usando Gauss-Seidel

Routing of packets has been the primary method by which data have been transmitted across packet networks, such as the Internet. When multiple users are interested in the same data the same source, multicast routing is typically used. However, routing alone is not sufficient to achieve the multicast capacity of a network. Ahlswede at [1] showed that using network coding that requires intermediate nodes to encode incoming data a multicast capacity equal to the minimum of the min-cut capacities of all the receivers can be achieved. This multicast capacity cannot be achieved using only routing (with or without replication). There has been a lot work going on to use network coding for multicast in satellite systems.

说明：  一种新颖的启发式优化算法：飞蛾扑火算法（MFO）(A novel heuristic optimization algorithm: moth flame-fighting algorithm (MFO))

Power flow analysis with TCSC using Newton- Raphson method

说明：  状态观测器Simulink 模型，可用于弹簧阻尼系统的仿真及系统状态观测(State observer simulink)

程序实例，以简洁高效的代码来实现许多程序的功能，是初学者快速入门的经典教材。(procedures matlab examples, concise and efficient code to achieve a number of procedures and functions, are beginners QuickStart classic materials.)

PCA:Principal Components Analysis It computes Principal Component Analysis, i.e., the linear transform which makes data uncorrelated and minize the reconstruction error.

Application for pre-processing and feature extraction for leaf recognization, this contains sample leaves and code to save it to database and extract features of each leaf

说明：  几种前向神经网络的应用实例，用matlab编的源代码(Several prior to the application of neural network, using matlab made the source code)