matlab

qpskdemod(绝对经典、不可多得的好原程序）(qpskdemod (absolute classics, and rare good original program))

its a matlab program ~~~~~ you can do tit

数字信号处理中，几个实验的matlab原代码。(Digital signal processing, Matlab several experiments the original code.)

说明：  故障时频域特征提取，前六阶CEEMDAN+初始信号时频域特征(Fault time-frequency domain feature extraction, first six-order CEEMDAN + initial signal time-frequency domain feature)

说明：  包含两种短时傅里叶变换程序跟反变换程序 STFT、stft2是两个短时傅里叶变换程序 istft是短时傅里叶变换逆变换程序(There are two kinds of short time Fourier transform program and inverse transform program STFT and stft2 are two short time Fourier transform programs ISTFT is a short time Fourier transform inverse program)

说明：  遗传算法 多目标优化算法，SPEA2 主要流程：1：首先初始化一个archive的集合P1，以及一个常用的population的集合P。设置P1的大小是N1，P的大小是N。初始的时候P1是空的，而P是已经初始化好的种群个体。 2：对P1进行评价，选出来里面的pareto front为1（下面用PF1来代替了）的个体，copy到P1中，如果PF1的数量大于N1的话，这个接下来再说，目前假定PF1的数量小于N1。(Genetic algorithm, multi-objective optimization algorithm, SPEA2 Main process: 1: first, initialize a set P1 of archive and a set P of popular. Set P1 to N1 and P to n. At the beginning, P1 is empty, and P is the initialized population. 2: Evaluate P1, select the individual whose Pareto front is 1 (Pf1 is used to replace it below), copy to P1, if the number of Pf1 is greater than N1, then let's talk about it. At present, it is assumed that the number of Pf1 is less than N1.)

这里描述了模糊聚类的一些方法，书为英文，但是内容很充实，有例子给出应用说明。(Here describes a number of fuzzy clustering methods, the book is English, but the content is very substantial, there are examples of application notes are given.)

基于QR分解的波束成形，掐你条件是已知信道完全信息，然后对信道状态H进行分解，求出波束赋形矩阵(Beam forming based on QR decomposition, pinch your condition is known channel completely, and then decompose the channel state H, and calculate beamforming matrix)

在Matlab环境下用Gauss消元法求解线性方程组的根(In Matlab environment using Gauss elimination method for solving linear equations of the root)

核方法工具箱，包括核典型相关分析KCCA，KPLS等算法的源码和实现。(Kernel Methods Toolbox KMBOX includes implementations of algorithms such as kernel principal component analysis (KPCA), kernel canonical correlation analysis (KCCA) and kernel recursive least-squares (KRLS). )