chap7

可求最短路径和最小边覆盖颠覆盖和旅行商问题的图论程序，用matlab编写(Rectifiable shortest path and minimum edge covering and traveling salesman problem of subversion to build the graph theory procedures prepared by matlab)

里面是关于mybatis的基本介绍已经附带的demo。(Inside is a basic introduction to mybatis already incidental )

说明：  可支持matlab使用SVM支持向量机，结合粒子群算法。(Support matlab to use SVM support vector machine)

说明：  Book on power amplifier

瞬时盲源分离 源代码 Instantaneous blind source separation(Instantaneous blind source separation)

压缩感知的BP MP OMP重构算法比较，包括运行时间、误差、PSNR的比较，稀疏表示为小波变换(compressed sensing reconstruction algorithm comparing BP MP OMP, including comparative run-time error, PSNR ，sparse representation is the wavelet transform)

The function applies the Madsen method for Doppler Centroid estimation. The input are: 1) the raw data, the parameter of the distance between samples in azimuth to be correlated and the PRF (Pulse repetition frequency)

说明：  经典程序例子,matlab语言编制的。 可供学习者参考(Classic example of the procedure, matlab languages. For learner reference)

说明：  盲源分离FastICA、matalab程序，(FAST KERNEL ICA | |--------------------| Version 1.0- February 2007 Copyright 2007 Stefanie Jegelka, Hao Shen, Arthur Gretton This package contains a Matlab implementation of the Fast Kernel ICA algorithm as described in [1]. Kernel ICA is based on minimizing a kernel measure of statistical independence, namely the Hilbert-Schmidt norm of the covariance operator in feature space (see [3]: this is called HSIC). Given an (n x m) matrix W of n samples from m mixed sources, the goal is to find a demixing matrix X such that the dependence between the estimated unmixed sources X *W is minimal. FastKICA uses an approximate Newton method to perfom this optimization. For more information on the algorithm, read [1], and for more information on HSIC, refer to [3]. The functions chol_gauss and amariD are taken from and based on, respectively, code from Francis Bach (available at http://cmm.ensmp.fr/~bach/kernel-ica/index.htm). The derivative is com)

基于时间序列法的风电预测matlab程序，可根据已知数据预测未来的风电功率。(Method based on time series prediction matlab program for wind power, according to known data to predict the future wind power.)