LDPC-matlab

CONSX—这是一个主要子程序，调用其它于程序及输出中间结果 * !* CHECK—检查所有的点是否满足约束条件,对违背约束的点进行校正 * !* CENTR—计算中心点 * !* FUNC —目标函数，由用户提供 * ( PROGRAM COMPLEX PARAMETER(N=3,M=4,K=6) DIMENSION X(K,M),R(K,N),F(K),G(M),H(M),XC(N) INTEGER GAMMA OPEN(4,FILE= COMPDATA ) READ(4,*)ITMAX,IPRINT,ALPHA,BETA,GAMMA,DELTA READ(4,*)(X(1,J),J=1,N) IX=2097151 YFL=0. DO 100 II=2,K DO 100 JJ=1,N CALL RANDU(IX,YFL) R(II,JJ)=YFL 100 CONTINUE OPEN(2,FILE= COMPDAT.OUT ,STATUS= UNKNOWN ) WRITE(2,10) 10 FORMAT(//,10X, COMPLEX PROGRAM OF BOX ) WRITE(2,11)N,M,K,ITMAX,IPRINT,ALPHA,BETA,G )

完整精通MATLAB综合辅导与指南书籍，网上有些缺少前4章。(Full proficiency in MATLAB integrated counseling and guide books, the Internet and some lack of pre-Chapter 4.)

本文设计了低噪声LNA放大器，较好了达到了指标，但是仍需改进。(This article is designed LNA low-noise amplifier has achieved a good target, but still needed improvement.)

说明：  本程序实现了信号稀疏分解的经典算法OMP！(This program implements a classic signal sparse decomposition algorithm OMP!)

关于无线网络MATLAB仿真经典教材的代码，适合有一定基础的同志们学习(MATLAB simulation of the classic textbook wireless network code for a certain comrades based learning)

tfrstft和tfrgabor不同窗函数，不同窗长度的时频性能分析。(tfrstft and tfrgabor different window function different window length, time-frequency analysis of performance. Note: The required frequency toolbox)

POD本征正交分解POD本征正交分解POD本征正交分解POD本征正交分解(POD POD proper orthogonal decomposition of the orthogonal decomposition POD proper orthogonal decomposition POD of the orthogonal decomposition)

This is code test to test the uploading features in this platform

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

支持向量机实现代码！(Support vector machine to achieve code!)