three

费聂耳衍射成像的模拟，采用透镜成像原理，有效地利用fft(simulation of the diffraction，based on the optical lens and the theory of fft)

fdtd2D simulationby matlab

《MATLAB神经网络30个案例分析》中的第22个例子，案例22 LVQ神经网络的预测——人脸朝向识别。希望对大家有一定的帮助！ (The MATLAB neural network analysis of 30 cases of 22 example, 22 cases of LVQ neural network prediction, face toward the identification. Hope to have certain help to everybody!)

遗传算法（Genetic Algorithm）matlab工具箱，内含很多matlab例程，对于学习遗传算法很有帮助。(Genetic Algorithm (Genetic Algorithm) matlab toolbox, containing many matlab routines, useful for learning genetic algorithm.)

最大似然（ML）准则和最大后验概率（MAP）准则，毕设内容，高光谱图像基本处理，现代信号处理中谱估计在matlab中的使用。( Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, Complete set content, basic hyperspectral image processing, Modern signal processing used in the spectral estimation in matlab.)

通信仿真课的源代码及具体报告设计，希望对大家有用(Communication simulation class' s source code and the specific design of the report, want to be useful)

用于分类的主判别变量算法(principal discriminant variate algorithm) ( principal discriminant variate algorithm for two classes, x1 and x2. x1 is the training samples in the 1st class with each column being the spectrum characterizing the corresponding sample. x2 is the training samples in the 2nd class with each column being the spectrum characterizing the corresponding sample. lamda is the penalty weight defining the balance between FLDA and PCA. dvnum is the number of principal discriminant variates to be computed. A defaul value for dvnum is 2. dv is the principal discriminant variates. scores is the matrix with each row being the scores of the samples on the corresponding principal discriminant variate. xmean is the mean spectra of the sample, which was used for mean-centering the data in training and prediction. )

BUSSYSTEME IN DER FAHRZEUGTECHNIK

实现了广义预测控制的算法，里面有具体的说明文档(Implementation of the generalized predictive control algorithm, which have specific documentation)

说明：  bp网的程序，设计一个神经网络字符分类器对英文字母进行分类。输入向量含有16个分量，输出向量分别用设置的T向量，还不完善，有待改进(bp network program, character design a neural network classifier to classify the English alphabet. Input vector contains 16 components, respectively T vector output vector set, not perfect, needs improvement)