Tri-training算法集合程序

matlab教程ppt 绝对实用 简介明快 一看就懂(the ppt about matlab)

modelisation de l attenuation d une onde

说明：  考虑了孤岛型微电网的灵活性，在成本最低的条件下，进行优化调度(Considering the flexibility of islanded microgrid, the optimal dispatch is carried out at the lowest cost.)

说明：  设计一八木天线，振子数目为4，长度分别为：反射器长度0.39，主馈振子长度0.356，引向器长度均为0.3；反射器和主馈振子的距离0.265，主馈振子和第一引向器的距离0.2575，第一引向器和第二引向器的距离0.225。天线工作频率为400MHz，各振子分段数为9。 利用矩量法模拟该天线的电流分布，并绘制方向图。(A eight wood antenna is designed. The number of vibrators is 4 and the length is 0.39 for the reflector, 0.356 for the main feeder and 0.3 for the director. The distance between the reflector and the main feeder is 0.265, 0.2575 for the main feeder and the first director. The distance between the first director and the second director is 0.225. The working frequency of the antenna is 400MHz and the number of segments of each vibrator is 9.)

二维的LDA+PCA人脸识别matlab程序，都可以直接使用。(Two-dimensional LDA+ PCA face recognition matlab procedures can be used directly.)

查询公交，查询公交，查询公交，查询公交，查询公交，查询公交(Search bus)

feature description using sift

With the increasing penetration of renewable energies as distributed generation (DG) in smart grid scheme, technical problems arise in both distribution and transmission system level. Undesirable voltage is the main problem for connection of DGs in distribution systems while excessive reactive power demand transmission system is the major concern for Transmission System Operators. The low power generation capacity of DER (Distributed Energy Resources) has motivated the need for integration of different types of DERs and loads in the form of micro-grid to enhance the power generation capacity, reliability and marketability of dispersed type of micro-sources [12, 13]. Current harmonics and Reactive power consumption are the two power quality issues that are addressed in this thesis

采用偏最小二乘法，在matlab R2009b调试通过，进行波形数据分析,复化三点Gauss-lengend公式求pi，DC-DC部分采用定功率单环控制，最小均方误差（MMSE）的算法。

梯度下降法是一个最优化算法，常用于机器学习和人工智能当中用来递归性地逼近最小偏差模型(The gradient descent method is an optimization algorithm used in machine learning and artificial intelligence to recursive approximation of the minimum deviation model )