LS-SVMlab1.5(advanced)

频域捕获，码相位，载波频率确定，时间变短(Capture frequency, code phase, carrier frequency to determine)

用曲线拟合的方式估计线性调频信号的初始频率及其调频斜率。得到的效果比较好。(Curve fitting way to estimate the initial frequency and chirp rate of the linear FM signal. Get better.)

当前的很多海洋数据都是海量数据，传统的ftp下载费时费磁盘，因此，openDAP方式越来越多，通过opendap可以选择直接需要的时间、空间范围内的资料。nctoolbox结合matlab实用，效果非常好的opendap工具包(NCTOOLBOX- 20130305 Full instructions and documentation at: http://code.google.com/p/nctoolbox/ We are in the progress of moving the project hosting here to github.com. Brief summary: *Prequisites *Matlab R2008a+. You can verify the version of Matlab by typing: version *Java 6+. You can verify the version of Java used by Matlab by typing: version( -java ). *The version returned should start with Java 1.6 . If it starts with Java 1.5 you can try updating the Matlab JVM: http://www.mathworks.com/support/solutions/en/data/1-1812J/ or use the older nctoolbox-20091112 version of this toolbox. )

很典型的求解线性矩阵不等式的程序，非常简单，对于学习求解线性矩阵不等式很有帮助(Typical procedure for solving linear matrix inequalities, very simple, for learning useful for solving linear matrix inequalities)

说明：  MATLAB课程的学习课件，梳理了书中的重点难点，可供大家参考，节约时间(MATLAB course of study courseware, combing the focus of the book difficult, for reference, save time)

核Fisher鉴别分析方法（KFDA）。KFDA方法的基本思想是首先将原始训练样本通过一个非线性映射 映射到某一高维(可能是无限维)特征空间F中，然后在F中执行Fisher鉴别分析。(Nuclear Fisher discriminant analysis method (KFDA). The basic idea of KFDA method is the first original training data by a nonlinear mapping  mapped to a high dimensional (possibly infinite dimensional) feature space F, then F in the implementation of the Fisher discriminant analysis.)

part 1 solve schrodinger......

给出了经典的粒子群优化算法的matlab实现方法，并结合测试函数，显示算法的寻优能力(Gives a classical particle swarm optimization algorithm matlab implementation, combined with the test function, the ability to display optimization algorithm)

说明：  MPSK信号基于高阶循环谱估计载波频率matlab的代码(MPSK signals based on higher-order cycle of the carrier frequency spectrum estimation matlab code)

说明：  Chebyshev 用切比雪夫多项式逼近已知函数 Legendre 用勒让德多项式逼近已知函数 Pade 用帕德形式的有理分式逼近已知函数 lmz 用列梅兹算法确定函数的最佳一致逼近多项式 ZJPF 求已知函数的最佳平方逼近多项式 FZZ 用傅立叶级数逼近已知的连续周期函数 DFF 离散周期数据点的傅立叶逼近 SmartBJ 用自适应分段线性法逼近已知函数 SmartBJ 用自适应样条逼近（第一类）已知函数 multifit 离散试验数据点的多项式曲线拟合 LZXEC 离散试验数据点的线性最小二乘拟合 ZJZXEC 离散试验数据点的正交多项式最小二乘拟合 (Interpolation function)