pLSA

说明：  遗传算法几个好的工具箱，给大家分享，希望互相交流(Several good genetic algorithm toolbox, to share with you the hope that mutual exchanges)

说明：  这是一个基于M/M/S类型的排队论仿真程序。M/M/S表示到达时间为负指数分布，服务时间为负指数分布，服务设备数量为S个的排队系统。 （1）到达模式：严格意义上讲是一个接着一个到达，而且到达时间间隔服从参数为mean_arr的负指数分布； （2）服务模式：服务台的数量为server_num,且每个服务台服务一个顾客的服务时间服从参数为mean_serv的负指数分布； （3）排队规则：满足先进先服务，服务快的先离开的规则。 (This is a based on the M/M/S types of queuing theory simulation program. M/M/S said time of arrival of the negative exponential distribution and service time for the negative exponential distribution, service equipment for the number of S-queuing system. (1) mode of arrival : a strict sense is a one followed arrived, but the inter-arrival time parameters mean_arr obey the negative exponential distribution; (2) mode : The number of help desk server_num. with each help desk services a customer service parameters mean_serv obey the negative exponential distribution; (3) queuing rules : first meet advanced services, the first to leave fast rules.)

Electric Machines Analysis and Design Applying MATLAB

Parzen窗法，是Parzen的窗口算法，用MATLAB语言编写的，用于随机概率的估计(Parzen window is Parzen window algorithm, using MATLAB language, probability estimates for random)

说明：  图像摆正，把任意角度的图像摆正Straighten images, straighten out images of the arbitrary Angle(Straighten images, straighten out images of the arbitrary Angle)

MATLAB GUI 简单的文本框数据传递(Simple Text Box Data Transfer in MATLAB GUI)

一维波动偏微分方程方程，差分法显式格式求数值解(One dimensional wave equation partial differential equations, difference explicit format Solving the Numerical Solution)

粒子群优化算法。PID控制器的整定，matlab源码。(Tunning of PID controller using Particle Swarm Optimization Author: Wael Mansour (wael192@yahoo.com) MSc Student, Electrical Enginering Dept, Faculty of Engineering Cairo University, Egypt)

模糊 C-平均(FCM)算法 验证过，可以运行。(Fuzzy C-mean (FCM) algorithm)

该代码采用matlab和C混合编写，是采用随机梯度的方法训练线性分类器，它可用于数据、图像等的多类分类情况。参考论文：Good Practice in Large-Scale Learning for Image Classification(This a Stochastic Gradient Descent algorithm used to train linear multiclass classifiers. It is biased towards large classification problems (many classes, many examples, high dimensional data).)