资源列表
随机森林
- RandomForestMatlabVersion
遗传神经网络案例
- 关于遗传神经网络的源代码并有案例，有参考价值
支持向量机，用于分类和预测
- 一类支持向量机，用于分类和预测，里面代码使用matlab编程,One-Class Support Vector Machine for classification and prediction, which code to use matlab programming
进行图像分解和图像融合的拉普拉斯金字塔matlab源程序
- 进行图像分解和图像融合的拉普拉斯金字塔matlab源程序,Image decomposition and the Laplacian pyramid image fusion matlab source
用opencv类库两副图像的特征点匹配
- 两副图像的特征点匹配，其中用了opencv类库，功能包括：特征点的亚像素精度检测，特征点初始匹配，基本矩阵计算及对极约束引导匹配等。matcher.rar网上的都是有错误，而且缺少类库，我把错误改正了，把所有需要的类库都加上了，现在已经编译通过，保证能运行！希望大家喜欢,2 points, matching the characteristics of images, which used the opencv library, features include: Feature Point D
ultimate.rar
- 配电网规划的主程序,根据目标函数中的建设费用,在通过潮流计算得到的运行费用,得到使此两者之和最小的网架.,The main power distribution network planning, in accordance with the objective function of the construction costs, the trend in the adoption of the operating costs have been calculated by making bo
MTALABandsimulink
- 用四阶龙格库塔法求非线性系统的输入相应，同时用simulink建模比较。 -Using fourth-order Runge-Kutta method for the corresponding input nonlinear systems, and modeling using simulink comparison.
bcs-spl-1.5-1.tar
- 一种适用于图像的压缩感知采样策略和重建算法，采样策略是基于块的图像稀疏采样矩阵，重建算法为smoothed projected Landweber（SPL）迭代算法。-BCS-SPL combines block-based compressed-sensing sampling (BCS) of an image with a smoothed projected-Landweber (SPL) iterative reconstruction. Sampling is driven by r
SIFTMatlab
- sift的纯Matlab程序 不包含siftWin32.exe文件
利用de Boor算法绘制B样条曲线
- 运用计算机图形学的知识，采用4阶B样条基函数，节点向量取为[0,0,0,0,1/4,2/4,3/4,1,1,1,1] 先绘制控制多边形，再用分段直线段绘制B样条曲线(Using the knowledge of computer graphics, the 4 order B spline basis function is used, and the node vector is taken as [0,0,0,0,1/4,2/4,3/4,1,1,1,1]. First, draw
DOE (2)
- 衍射光学光束整形元件设计 目标：在透镜后焦面上实现图形的光强分布 实现： 1、波长、衍射光学元件口径、形状、入射波前、二维目标分布及尺寸等都可自行设定； 2、对相位进行8台阶量化，重新计算； 3、更换输出面的采样间隔，重新计算。(Design of Diffraction Optical Beam Shaping Element Objective: To realize the intensity distribution of the pattern on the focal pla
CHN_alt
- GIS基础数据，DEM高程图，可用于标准地图制作(GIS basic data, DEM elevation map, can be used for standard map production. High-resolution original images are very precious)