Elaslic-finite-finite

读取MIT-BIH数据库，每个人的心电数据。(Read the MIT-BIH database, everyone' s ECG data.)

弗洛伊德算法计算有向网络图最短路问题。可算最短路径。(Freud algorithm to calculate the shortest path problem to the network diagram. Can be considered the shortest path.)

r 双精度实型一维数组,存放Yule-Walker方程的元素r(0),r(1), r(p), p AR模型阶数, a AR模型系数a(()

说明：  该压缩文件是《深度学习之Tensorflow的入门、原理及进阶实战》，里面讲述了如何搭建TensorFlow环境，并讲述了深度学习的一些理论基础知识，而且通过例子进行辅助，能更好的理解掌握。(The compressed file is "Introduction, Principle and Advanced Combat of Tensorflow for Deep Learning", which describes how to set up the TensorFlow environment, and describes some theoretical basic knowledge of deep learning, and assists with examples to better understand .)

bp网络实现认知无线电的检测和预测,从而达到了对频谱的分配()

三维风场模拟.利用Kaimal spectrum结合FFT进行风场模拟，生成风速时程得进行必要参数的定义。(3D Turbulent Wind Generation。 Generation of three-dimensional turbulent wind fields, by employing a Kaimal spectrum and IEC-based coherence function. )

改进DV-Hop定位算法 首先设置初始量，布置了一个范围为100×100m2的区域，其上随机分布100个传感器节点，其中有10个信标节点，节点的通信半径为30m。 第二步在正方形区域内产生均匀分布的随机拓扑，随机产生节点坐标并将其中十个选定为信标节点，其余九十个设为未知节点，然后画出节点分布图。 第三步通过最短路径法计算未知节点与每个信标节点的最小跳数。 第四步根据前面记录的其他信标节点的位置信息和相距跳数估算平均每跳的实际距离，用跳数估计距离的方法得出未知节点到信标节点的距离。 第五步用极大似然估计法求未知节点坐标 (Improved DV-Hop localization algorithm first set the initial amount, layout 100100m2 the area of ​ ​ a range of 100 sensor nodes randomly distributed on the 10 beacon node, the node communication radius of 30m. The second step in the square area to generate uniformly distributed random topology, random coordinates of the nodes and ten of the selected beacon node, the remaining 90 is set to unknown node, and then draw the node distribution diagram. The third step is to calculate the minimum number of hops of the unknown node and each beacon node through the shortest path method. The fourth step according to the location information of the other beacon nodes in the previous record and away from hops to estimate the average hop distance and hop count to estimate the distance to come to the distance of the unknown node to beacon nodes. The fifth step maximum likelihood estimation method and the unknown coordinates of the nodes)

基本蚁群算法寻找食物,需要TURBOC2运行环境,在vista下要兼容模式运行()

多变量灰色模型(Multivariable Grey Model,MGM)MGM(1,n)是研究复杂灰色系统的典型模型,其形式是n元一阶常微分方程组,是单点GM(1,1)模型在多点情况的扩展。(Multivariable grey model MGM (1, n) is a typical model for studying complex grey system. Its form is n n first order ordinary differential equations. It is the expansion of single point GM (1,1) model in multi-point case.)

一份详细讲述聚类的文章,内容是关系聚类和混合聚类(A detailed article on clustering, on relational clustering and hybrid clustering)