str2poly

说明：  语音信号的matlab处理程序,很多种，大家验证一下(Matlab voice signal processing, are many, we test)

这是一个用在Matlab中对二维图像进行特征点提取的代码(This function returns IMAGE s SIFT keypoints)

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.)

C均值算法的实现，这是我正在学习的C均值算法的实习代码，分享给大家，不当之处，欢迎指正(C-means algorithm, which I am learning C-means algorithm internship code for everyone to share, inappropriate, please correct me)

本光盘为《详解MATLAB快速入门与应用》一书的配套光盘，包括以下内容： 1.“sample”文件夹：提供了本书各章范例的源程序。(The CD is " Getting Started with MATLAB Detailed application," a book supporting the CD, including the following:. 1 " sample" folder: each chapter provides examples of the book source.)

实现了灰色理论在关联度识别方法在matlab中的应用(Grey achieved recognition in the correlation of in matlab)

细菌觅食算法的matlab程序，可以正确运行(Matlab procedure for bacterial foraging algorithm)

电力系统200KW太阳能电池接电力系统, matlab图解. 我同时分享了另一个300kw系统图，两者区别在于控制系统，可以对比一下。 (200-kW Grid-Connected PV Array (Average Model))

一种流形学习算法（很好用），包括主成分分析、因子分析、贝叶斯分析，包含收发两个客户端的链路级通信程序。( A fluid manifold learning algorithm (good use), Including principal component analysis, factor analysis, Bayesian analysis, Contains two clients receive link-level communications program.)

说明：  k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。(The k-means algorithm accepts the input k; then divides n data objects into k clusters in order to make the obtained cluster satisfy: the similarity of objects in the same cluster is high; and the similarity of objects in different clusters Smaller.)