tigaoyunx3

自己制作的光伏PV发电求线路损耗的Matlab/simulink模块，通过改变参数可得出不同地点处的功率损耗。(Produce their own photovoltaic PV power generation demand line loss of Matlab/simulink module parameters can be obtained by changing the Office of the power loss in different locations.)

孙志忠书：偏微分方程数值解，第三章第一节：抛物方程的有限差分解法向前欧拉格式(forward euler difference scheme for parabolic problem)

一个简单的PQ控制模型。利用直流电源当电源。希望能有帮助。(A simple control model PQ. When using a DC power supply. I hope to have help.)

这是由罗华飞编著的MATLAB GUI设计学习手记第二版的电子书籍(This is LuoHuaFei compiled by MATLAB GUI design study the second edition of the journal of electronic books )

语音识别，利用matlab hmm 实现语音识别(hmm matlab)

Numerical Methods Newton-Raphson

Matlab用小波神经网络变换对时间序列信号进行预测(Matlab neural network with wavelet transform to predict the time series signal)

本程序为小波在矩阵方程求解中的应用例程。(This procedure for the wavelet in the matrix equation in the application of routine)

克里金插值的matlab代码及其说明文档：强人学习克里金插值方法的matlab代码，其中含有使用文档，原理说明，以及代码注释！(The kriging interpolation matlab code and its description document: strongman learning kriging interpolation method matlab code, which contains the use of the document, Rationale, and code comments!)

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)