利用BP神经网络进行字符识别

在MATLAB里，对数据进行拟合，在双对数坐标下，看数据是否符合幂律分布，求出幂指数，并绘出图形。(In MATLAB, the pairs of data fitting, in double logarithmic coordinates to see whether the data meet the power-law distribution, find the power index, and draw the graph.)

常用决策树算法C4.5的实现代码。利用matlab实现。(C4.5 decision tree algorithm used in the realization of the code. Realize the use of matlab.)

枝切法sar数据处理，相位解缠，复图像配准(Branch cutting SAR data processing, phase unwrapping, complex image registration)

说明：  数据挖掘中的binning算法，用于数据预处理(Binning algorithm in data mining for data preprocessing)

将数据集转换为拉普拉斯矩阵，然后利用基于图论的谱聚类进行聚类。拉普拉斯矩阵采用高斯核函数，全连接方法计算。谱聚类擅长处理高维数据或非凸数据集。(The data set is transformed into Laplacian matrix, and then clustered by spectral clustering based on graph theory. The Laplacian matrix is calculated by using the Gauss kernel function and the full connection method. Spectral clustering is good at dealing with high-dimensional or non-convex data sets.)

利用radon变换寻找直线和直线倾斜的角度然后根据倾斜的角度进行旋转图片(Using radon transform and the straight line to find the angle of tilt of the tilt angle and then rotate picture)

集成地图的编程方法,地图算法中使用, 很有价值的参考,()

此程序为研究异形柱最不利荷载角的C++程序(This program is a C program for studying the most unfavorable load angle of special-shaped columns.)

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()