TCM_state_eq0

陆吾生有关凸优化视频教程中的相应的子函数，包括牛顿法等(convex optimization)

matlab关于数据的预处理的一些程序，很全，中心化，归一化，多元散射校正(matlab on some pre-program data, very wide, centralized, normalization, multiplicative scatter correction)

关于距离扩展目标-MN检测方法的matlab实现程序(Matlab on goal from the extended-MN detection method to achieve program)

鉴于LCD方法存在的问题，本文提出了一种基于互相关匹配端点延拓局部特征尺度分解（Cross-correlation matching endpoint Extension Local Characteristic scale Decomposition，简称CELCD），由于LCD分解原理是依据信号的局部极值点信息不断进行筛分信号，在信号分解时需要先确定信号的局部极值点，而信号的两个端点可能不是极值点，因此在信号两端点就会出现虚假成分，且该现象随着分解的进行向数据内部扩散，产生端点效应，导致分解结果失真。而CELCD方法的本质在于探索待分解信号前后端的数据规律，延长波形，使得端点效应不出现在原始信号分解的片段上，从而消除端点效应。(Given the LCD method has a problem, this paper presents a cross-correlation based on local feature matching endpoint extension scale decomposition (Cross-correlation matching endpoint Extension Local Characteristic scale Decomposition, referred CELCD), since the LCD decomposition principle is based on the signal of local optima ongoing screening point information signal, when the signal decomposition needed to determine local extreme point of the signal, and the two endpoints signals may not be extreme point, so the two end points of the signal will be fraudulent, and that the phenomenon with decomposition of the data in the internal diffusion, resulting end effect, leading to decomposition distortion. The essence of the method is to explore CELCD data signal to be decomposed before and after the end of the law, to extend the waveform, so the end effect does not appear on the original signal decomposition fragments now to eliminate the end effect.)

离散傅里叶变换、离散余弦变化、离散小波变换(Discrete Fourier transform, discrete cosine transform and discrete wavelet transform)

用bp网络解决xor问题——智能计算大作业(Bp xor problem with Network Solutions- Great job on Intelligent Computing)

matlab中文教程,matlab基础教程，起步教程(MATLAB basic course, suitable for beginners for reference, including a large number of examples)

LBER和LMS算法的功能仿真，图形象地描绘出了两种自适应算法随着单个信号传输的收敛过程，直至系统达到稳定状态。从图中看到LMS算法的收敛速度小于LBER算法并且达到稳态时的系统误码率远大于LBER算法。因此论文分析的自适应最小误码率多用户检测算法比自适应最小均方误差多用户检测算法性能优良

可以供在SVM门口的初学者早日入门，这些都是最基本的SVM程序，但是也比较专业，希望有需要的人使用(SVM can be at the door for an early entry for beginners, these are the most basic SVM procedure, but more professional, I hope the person has required the use of)

通过轨道不平顺值，求得不平顺的标准偏差，计算轨道质量指数。(Value by track irregularity, irregularity of the standard deviation obtained, the quality index calculated orbit)