OPF

逻辑运算例子！包括网络设计图和运算演算过程！ (failed to translate)

说明：  matlab 实用指南 实例M文件(Matlab Practical Guide document examples M!)

％ 该Matlab程序基于牛顿－拉夫逊算法，用于计算已知导纳矩阵、PQ节点、PV节点、平衡节点(UA)的电力网络潮流 ％ U - 各节点母线电压 S - 各节点注入功率 S_net - 电力网络总损耗 ％ PQ_P - 实算PQ节点注入有功功率 PQ_Q - 实算PQ节点注入无功功率 ％ delt_PQ_P - 实算PQ节点有功功率修正值 delt_PQ_Q －实算PQ节点无功功率修正值 ％ delt_UA_P - 实平衡节点有功功率修正值 delt_U_2 - 实平衡节点电压平方修正值 ％ delt_PQV - 实算P Q U^2修正值 J - 雅可比矩阵 ％ e - 电压实部 f - 电压虚部 delt_ef - 电压实部与虚部修正值( The Matlab program based on the Newton- Raphson algorithm is used to calculate the admittance matrix is known, PQ node, PV node, the node balance (UA) the trend of the electricity networks U- the node bus voltage S- each node injection power S_net-- The total loss of electric power networks PQ_P- operator PQ is injected into active nodes PQ_Q- operator PQ is injected reactive power node delt_PQ_P- is considered the correct value of active power PQ node delt_PQ_Q- really count PQ node reactive power to amend the value of delt_UA_P- real active power balance of the correct value of the node delt_U_2- node voltage balance is the correct value of the square of delt_PQV- really count PQU ^ 2 the correct value of J- Jacobian matrix e- voltage real part f- the imaginary part of voltage delt_ef- voltage real part and imaginary part of the amendment value)

希尔伯特黄变换,包括固有模态的求解算法以及经验模态分解的算法。利用此程序可以对一个复杂的信号分解出其内涵的确定性信号。(Hilbert Huang transfermation)

SPARSE ESTIMATION Sparse signal recovery via L_1 minimization. This code is the basis in the L1 magic toolbox for sampling signals that are sparse in the time domain.

Mtlab toolbox containing useful m files to generate LPI signals

阵列信号处理中: 仿真圆形阵列天线的用法,对比了传统方法和MUSIC方法的性能(Array Signal Processing: Simulation of circular array antenna usage, compared to the traditional methods and the performance of MUSIC method)

it a SCFDMA MATLAB source code

灰度共生矩阵和灰度梯度共生矩阵的提取方式，是比较重要的纹理特征提取方法，用matlab实现的(The GLCM and gray gradient co-occurrence matrix extracted, texture feature extraction method, using matlab to achieve)

dct域变换，用于图像处理，最纯粹的变换过程，配有详细的步骤说明(dct domain transform for image processing, purest transformation process, with a detailed description of the steps)