LInjie-PCA

cs的omp恢复算法,使用恢复矩阵，观测矩阵(The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the di® erent forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.)

演示matlab曲线拟和与插直的基本方法，详细的工具箱可供编程人员使用(demonstration Matlab curve fitting and interpolation straight to the basic methods, detailed toolkit for programmers to use)

L-BFGS有限内存BFGS法,用于求解大规模无约束最优化问题(L-BFGS method for large scale unconstrained optimization)

建立vonkarman自相关函数的随机介质程序(A random medium program for establishing vonkarman auto correlation function)

说明：  太阳能光伏经过整流再进行并到交流电网的平台搭建(Solar photovoltaic is rectified and then built on the platform of alternating current network)

基于matlab平台的人造目标的SAR回波数据，是本人历经一个学期辛苦编制，基于光学法成像，对于没有实测数据的兄弟们，希望有所帮助(SAR raw data simulator)

特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。 我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下： 對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )

GA TOOLBOX USING C++

linear quadratic model

通过不同载荷分析船舶运动方程的不同情况，包含较多的水动力导数，同时考虑了波浪谱密度函数的影响，采用了最新的od45方程求解6自由度船舶运动方程(The ship motion equation is analyzed under different loads, including more hydrodynamic derivatives, and considering the influence of wave spectral density function, the latest od45 equation is used to solve the 6-DOF ship motion equation.)