zaosheng

自相关信号检测仿真程序。在数据处理过程中，首先要检测信号的存在和大致位置。(Self-related signal detection simulation program. In the data processing, the first to detect the presence and approximate location of the signal.)

基于神经网络估算断面客流量与出行量之间的OD矩阵(OD matrix estimation section between the amount of traffic and travel based on neural network)

matlab code for GRABIT

BFGS的matlab算法，大家可以自己看一下(methods of BFGS in matlab )

THE SYNTAX OF A PROGRAMMING LANGUAGE tells you what code it is possible to write—what the machine will understand. Style tells you what you ought to write— what the humans reading the code will understand. Code written with a consistent, simple style will be maintainable, robust, and contain fewer bugs. Code written with no regard to style will contain more bugs. It may simply be thrown away and rewritten rather than maintained.(var OF THE PROGRAMMING LANGUAGE tells A. you what code it is possible to write-what the ma chine will understand. Style tells you what you ought to write-what the humans reading the code will understand. Code written with a consisten t, simple style will be maintainable, and robust. and contain fewer bugs. Code written with no reg ard to style will contain more bugs. It may simpl y be thrown away and rewritten rather than maint ained.)

通过4阶龙格库塔方法求解混沌系统的微分方程(Through four order Runge-Kutta method for solving differential equations of chaotic systems)

模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却。 (Simulated annealing algorithm derived the principle of solid annealing, heating the solid to the full high, let it slowly cooling. )

清华模式识别张学工，第一次作业，基于身高体重数据进行性别分类(Pattern recognition, based on height and weight data for gender classification)

a simple transmit diversity technique for wireless communications

说明：  半监督学习(Semi-Supervised Learning，SSL)是模式识别和机器学习领域研究的重点问题，是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据，以及同时使用标记数据，来进行模式识别工作。当使用半监督学习时，将会要求尽量少的人员来从事工作，同时，又能够带来比较高的准确性，因此，半监督学习目前正越来越受到人们的重视。(Semi-Supervised Learning (SSL) is a key issue in the field of pattern recognition and machine learning. It is a learning method combining supervised learning with unsupervised learning. Semi-supervised learning uses a large number of unlabeled data, as well as labeled data, for pattern recognition. When using semi-supervised learning, it will require as few people as possible to work, and at the same time, it can bring relatively high accuracy. Therefore, semi-supervised learning is receiving more and more attention.)