CHAOTIC

说明：  最小二乘法的简单应用，以及算法实现，作为参考。(Simple application of least squares, and the algorithm, as a reference.)

Genetic algorithms in 8 queen problem

WSN 质心定位算法 Centorid Algorithm 加经典参考文献 A: 如果接受节点接收到的包数目与发送节点发送的包数目的比值大于CM_threshold,则这两个节点视为邻居节点 未知节点利用邻居锚节点进行定位，没有邻居锚节点的未知节点无法定位。 B: 如果接受节点接收到的包数目与发送节点发送的包数目的比值大于CM_threshold,则这两个节点视为邻居节点 未知节点一旦被定位就充当起锚节点的功能，向周围邻居发送自己的估计坐标信息 未知节点把已经定位的未知节点与锚节点同等对待 C: 如果接受节点接收到的包数目与发送节点发送的包数目的比值大于CM_threshold,则这两个节点视为邻居节点 有邻居锚节点的未知节点只利用邻居锚节点进行定位，没有邻居锚节点的未知节点才利用已经定位了的邻居未知节点进行定位。(WSN centroid localization algorithm Centorid Algorithm A: If you accept the received packet node number and the number of packets sent by the sending node ratio greater than CM_threshold, then these two nodes as neighbors Unknown node using the neighbor anchor nodes to locate, no neighbors anchor nodes and unknown nodes can not be located. B:   If you accept the received packet node number and the number of packets sent by the sending node ratio greater than CM_threshold, then these two nodes as neighbors Unknown nodes are positioned on as soon as the function of anchor nodes to send their neighbors estimated coordinate information Unknown unknown node to node already positioned anchor nodes treated equally C: If you accept the received packet node number and the number of packets sent by the sending node ratio greater than CM_threshold, then these two nodes as neighbors Of the unknown neighbor anchor node the neighbor nodes are only positioned anchor nodes, )

基于的BPSK的matlab，完整的程序，有能相关的说明和注释，有需要的朋友下载吧~(Based on the matlab BPSK, complete program and relevant instruction and comments, the need to download friends ~)

几种基本粒子群算法的matlab代码，逐步提升，有助于初学者。(Several basic particle swarm algorithm matlab code, and gradually improve, to help beginners.)

交互界面，检测图像中的直线，并计算交点、直线角度、长度、端点位置(Interaction interface, detects the straight line in the image, and calculates the intersection point, the straight line angle, the length and the end point position.)

说明：  关于改进的DLDA的算法，结合了人脸识别(DLDA on the improvement of the algorithm, a combination of face recognition)

　k-means 算法接受参数 k ；然后将事先输入的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。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。(K-means algorithm accept parameter k Then will the n of prior input data object is divided into k clustering to make won clustering meet: the same clustering the object in the similarity is higher And different clustering the object in the smaller similarity. Clustering similarity is using the cluster of the object in the mean gained a "center object" (the center of gravity) to calculate. )

power tracking ac source with arduino

说明：  EM算法作为机器学习的经典算法，本算法为EM源程序，陪带实验(EM algorithm as the classic machine learning algorithms, the source of the EM algorithm, to accompany with the experimental)