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说明：  In computer science, differential evolution (DE) is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristics such as DE do not guarantee an optimal solution is ever found.

基于粒子群优化算法（PSO）的类间方差估计（Otsu）(Based on Particle Swarm Optimization (PSO) of the between-class variance estimation (Otsu))

一个数据预处理过程中消除多项式趋势项的程序(this is a program about eliminating polynomial trend)

正交频分复用（OFDM）的完整系统仿真程序，值得学习参考(Orthogonal Frequency Division Multiplexing (OFDM) is a complete system simulation program, it is worth learning reference)

细菌觅食优化算法源代码，简单易懂，适合初学者(Bacteria foraging optimization algorithm source code, easy to understand.)

matlab在运筹学中的应用 的教材（含源码）(matlab in the application of operations research in the teaching materials (including source code))

maze solved with q learning

全相位陷波器是全相位数字信号处理体系中一个很有特色的技术，在通信、军事等抗强干扰场合中应用及其广泛，因而研究全相位陷波器并仿真建模实现其滤波功能，具有很高的意义。(All-phase notch filter is a very distinctive technique in the all-phase of digital signal processing system. In communications，military and such resistance interference are widely used. Therefore, it is of high significance to the study of All-phase notch filter and simulate the modeling to realize the filter function)

实现单个卫星导航仿真，可定位分析出目标物的方位(Simulation for single satellite navigation, can locate the position of the target)

EM算法在神经网络中的应用，可以用来进行视频数据分类。(In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.)