RBFMolding

说明：  移相全桥变换器的建模与仿真，希望对电力电子的同学有用。(The whole bridge converter phase shifting of the modeling and simulation, the hope of power electronic classmates useful.)

压缩感知的仿真算法，使用的是SOMP恢复算法。(Compressed sensing simulation algorithm, using SOMP recovery algorithm.)

说明：  对信号类型的说明，并画出其相应的波形，求出信号的功率和能量。(Description of the type of signal, and draw the corresponding waveform, find the power and energy of the signal.)

mittage 函数的matlab 实现 (mittage function with matlab)

Affinity Propagation聚类算法源码（Matlab）。內附两个测试用例（ToyProblem.mat，FaceClusteringSimilarities.zip）(Affinity Propagation Clustering algorithm source code and two dataset for testing)

AR随机过程的建模，给定一个AR过程，对其进行建模，分别利用Yule-Walker方程和协方差法进行功率谱估计(AR random process modeling, given an AR process, its modeling, respectively, Yule-Walker equations and covariance method of power spectrum estimation)

一个基于matlab的fsk仿真，加入了高斯白噪声(Fsk the matlab-based simulation, by adding a Gaussian white noise)

simulation in transientcdfr

说明：  植物虫害检测（GUI，注释，svm算法） 该课题为基于MATLAB SVM方法的植物病害检测系统，带GUI界面，可以识别多种被虫害侵蚀的植物叶子，输出结果。带论文和详细注释。 train 对黄瓜子文件夹所有图片提取 颜色矩特征和gabor纹理特征，然后svm训练 test 对测试图像灰度化，滤波，提取 颜色矩特征和gabor纹理特征，然后svm模型测试，输出类别 colorMom.m 颜色矩特征提取 Gabor_palm.m gabor纹理特征提取(Plant pest detection (GUI, annotation, SVM algorithm) The project is a plant disease detection system based on MATLAB SVM method, with GUI interface, which can identify a variety of plant leaves eroded by pests and output results. With paper and detailed notes. Train extracts color moment features and Gabor texture features from all the pictures in the sub folder of cucumber, and then SVM trains them Test grayscale and filter the test image, extract color moment features and Gabor texture features, then SVM model test, output category Colormom. M color moment feature extraction Gabor u Palm. M Gabor texture feature extraction)

说明：  （回归分析） 考察温度x对产量y的影响，测得下列10组数据： 求y关于x的线性回归方程，检验回归效果是否显著，并预测x=42℃时产量的估值及预测区间（置信度95 ） ((Regression analysis) x on the output of the temperature study of the impact of y, measured the following 10 sets of data: for y on x of the linear regression equation to test whether the effect of a significant regression and prediction when x = 42 ℃, and the valuation of yield prediction interval (95 percent confidence level))