neichagongshi

部分有关最优控制理论与应用的相关例题程序集(Part of the optimal control theory and application of relevant examples assembly)

Face Recognization algorith development in MATLAB

Classic data mining book

该程序运用matlab可实现电子云密度分布的可视化，(matlab solve electron cloud)

1． 该光盘包含了“信号与系统分析及MATLAB实现”一书的第八章所有程序； 2． 为了方便读者阅读程序，所有程序均采用中文注释。MATLAB的程序编辑器不支持中文显示，用户只需在Word下直接打开源程序文件，即可阅读程序中的中文注释； 3． 程序文件的命名分为以下三种类型： （1） 若为书中例题的程序，则程序文件名一律以ex开头，后面跟4位数字（前两位表示章号，后两位表示例题序号）表示的该程序所在例题的编号。例如，ex0603.m是例6.3的程序，而ex1102.m则是例11.2的对应程序。 （2） 若为书中图形的实现程序，则程序文件名一律以figure开头，后面跟4位数字（前两位表示章号，后两位表示图形序号）表示的该程序所绘图形的编号。例如，figure1203.m是实现图12.3的程序，而figure0710.m则是实现图7.10的对应程序。 （3） 若为函数文件，则直接以函数名命名，例如文件sconv.m即是书中函数sconv()的对应程序。 ()

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)

说明：  数据挖掘算法，support vector machine算法源代码，用于分类(data mining algorithms, support vector machine algorithm source code for classification)

lagrange search algorithm matlab

利用四阶龙格-库塔的数值方法编程实现传递函数(transfer function accomplishment)

程序展示了bayes classifier对于连续数据的应用，假设数据均服从高斯分布。程序包含了binary classification和multi-classification的例子。(Program shows bayes classifier for continuous data applications, assume that the data are Gaussian. Program includes a binary classification and multi-classification examples.)