FOC

根据空间矢量的原理，建立了svpwm的matlab/simulink模型，方便学习和使用(According to the principle of space vector is established svpwm the matlab/simulink model, easy to learn and use)

极限学习机（extreme learning machine）ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。2006年由南洋理工大学黄广斌副教授提出。传统的神经网络学习算法（如BP算法）需要人为设置大量的网络训练参数，并且很容易产生局部最优解。极限学习机只需要设置网络的隐层节点个数，在算法执行过程中不需要调整网络的输入权值以及隐元的偏置，并且产生唯一的最优解，因此具有学习速度快且泛化性能好的优点。(Extreme Learning Machine (extreme learning machine) ELM is an easy-to-use and effective single hidden layer feedforward neural network the SLFNs learning algorithm. 2006 by the Nanyang Technological University Associate Professor Huang Guangbin. Traditional neural network learning algorithm (BP) artificial network training parameters, and it is easy to generate a local optimal solution. Extreme Learning Machine network only need to set the number of hidden nodes, the algorithm implementation process does not need to adjust the network input weights and hidden element of bias, and only optimal solution, so the learning speed and generalization good performance advantages.)

PSPICS使用手册,详细介绍了PSPICE的使用方法，是一本很好的入门书籍。(examples for the use of pspice)

对实域信号加窗（矩形窗、汉明窗）做DFT分析信号频谱(The real field signal plus (rectangle window, hamming window) do DFT spectrum analysis)

说明：  图着色，采用图论的方法进行图形着色，首先采用相邻矩阵，计算顶点之间的关系(Graph coloring. Graph coloring is done by graph theory. First, the adjacent matrix is used to calculate the relationship between vertices.)

最后的测试程序，得出文本检索的正确率和召回率(In the end, the cosine similarity of texts is used to measure the similarity between the query and documents. The data comparison indicates that this algorithm has well experimental results. Moreover, it gets the advantage over the classic LSA retrieval algorithm in precision and operational efficiency.)

说明：   经典控制理论MATLAB-GUI设计(classical control theory MATLAB- GUI design)

对一种电极控制对象的最基本pid控制程序，仅供参考。(Electrode control object of a basic pid control procedures for reference purposes only.)

Here the study of the reactive power compensation in the grid connection of PV system.

find the probability of error