lvboqi

一个MATLAB的GUI界面编程实例，类似于一般的软件界面，开发环境MATLAB7.4（R2007b）(A MATLAB-GUI interface for programming examples, similar to the general software interface, development environment MATLAB7.4 (R2007b))

solar cell photovoltaik pv mat lab bir diyodlu model m-file

说明：  给出了一个有关Chaikin细分法的matlab编程，该程序适合于任意的封闭的初始控制多边形(a program about Chaikin subdivision scheme which can be applied to closed polygon)

comparison of alamouti scheme over rayleigh fading

这是电网、理论中实现矩阵快速变换的程序代码，附有使用说明书，技术说明(This is a grid, matrix theory, to achieve rapid transformation of the program code, with the use of brochures, technical notes)

（压缩包里一共有5个代码） pca+lda+粗糙集+模糊神经网络 saveORLimage.m将ORL人脸库分为测试集ptest和训练集pstudy存为imagedata.mat 1.savelda.m将人脸库先进行pca降维，再用lda进行特征提取，得到新的测试集ldatest和训练集ldastudy存为imageldadata.mat 2.对ldastudy进行离散化(discretimage.m)，得到离散化矩阵disdata,存入到imagedisdata.mat 3.将disdata组成决策表(savers.m)，通过对disdata的条件属性进行约简，得到其一个约简，组成新的测试集rstest和训练集rsstudy存为imagersdata.mat 4.对rsstudy进行模糊神经网络训练(savecul.m)，对模糊神经网络的参数进行调整学习将其存入culdata.mat 5.用runfnn.m对rstest进行测试得到其识别率 savem.m和cm.m是用最小距离分类器对训练集和测试集进行分类.(pca+ lda+ Rough Set+ fuzzy neural network saveORLimage.m will ORL face database is divided into test set and training set ptest for pstudy keep imagedata.mat Treasury will face 1.savelda.m first dimensionality reduction pca, lda used feature extraction, a new test set and training set ldatest for ldastudy keep imageldadata.mat 2. Ldastudy carried out on the discretization (discretimage.m), to be discrete matrix of disdata, deposited to imagedisdata.mat 3. Disdata the composition of the decision table (savers.m), the conditions on the attributes disdata about Jane, has been one of its reduction to form the new test set and training set rstest for rsstudy keep imagersdata.mat 4. Rsstudy training fuzzy neural network (savecul.m), on the parameters of fuzzy neural network to learn to adjust their deposit culdata.mat 5. Rstest used to test for runfnn.m by its recognition rate cm.m is savem.m and minimum distance classifier on the training set and test set classificati)

Steve Gunn 开发的svm工具箱源代码，用于分类和回归(Developed by Steve Gunn，used in classification and regression)

耳蜗实质上相当于一个滤波器组，耳蜗的滤波作用是在对数频率尺度上进行的，在1000HZ下，人耳的感知能力与频率成线性关系；而在1000HZ以上，人耳的感知能力与频率不构成线性关系，而更偏向于对数关系，这就使得人耳对低频信号比高频信号更敏感。Mel频率的提出是为了方便人耳对不同频率语音的感知特性的研究。频率与Mel频率的转换公式为(on logarithm frequency scale, under the 1000 hz, the perception of the human ear and a linear relationship with frequency In more than 1000 hz, the perception of the human ear does not constitute a linear relationship with frequency, and prefer to logarithmic relationship, which makes the human ear is sensitive to low frequency signal is better than high frequency signal. Mel frequency is put forward in order to facilitate the ear of the study of speech perception characteristics of different frequency. For frequency and Mel frequency conversion formula )

路径跟踪中心轨迹法，是一种求解最优潮流的有效方法，大大简化了原有算法的复杂性(Path Tracking Center locus method is an effective method for solving the optimal power flow, greatly simplifies the complexity of the original algorithm)

说明：  本代码实现了SVM分类算法实现寻找最佳的c和g。(SVM Classification Algorithms to Find the Best C and G)