研究生课程机器人控制基础大作业 检测直线与曲线

免疫算法求解函数优化问题运行时请选择所要的函数,并且点Button1执行-immune function algorithm optimization runtime please select the function and implementation point Button1

一个牛人的神经网络支持向量机程序,对于搞神经网络的朋友强烈推荐.第二部分-A wonderful program of svm code coming from a superman,and strong recommendation to someone who learn about pattern-recognition .The second part.

LinPacker是一个优化矩形布局的工具，是在具有遗传算法的半无限大小区完成这一操作的。对缩减工厂的库存是有用的。-LinPacker optimization is a rectangular layout of the tool, with a genetic algorithm in the semi-infinite size of the area to complete the operation. To reduce factory stocks will be useful.

基于canny的灰色预测和模式识别,标定的一种做法是：在一块平板上画若干个点， 同时用这两部相机照相，分别得到这些点在它们像平面上的像点，利用这两组像点的几何关系就可以得到这两部相机的相对位置。然而，无论在物平面或像平面上我们都无法直接得到没有几何尺寸的“点”。实际的做法是在物平面上画若干个圆（称为靶标），它们的圆心就是几何的点了。而它们的像一般会变形，如图1所示，所以必须从靶标上的这些圆的像中把圆心的像精确地找到，标定就可实现。-Based on the gray canny prediction and pattern recognition, calibration approach is to: a plate painted in a number of points, at the same time with these two cameras camera, respectively, as these points in their plane as points, the use of these two groups geometric relationship between the image point can be that the relative positions of two cameras. However, in terms of objects such as plane or plane, we can not directly be no geometry point. Actual practice is complex plane painted in a number of round (known as targets), their geometric center of a circle is the point. And they generally like deformation, as shown in Figure 1, it must target these round, like in the center of a circle of like to find precise, calibration can achieve.

SOM自组织神经网络的C语言源码，可以通过该程序实现无监督的聚类。-SOM self-organizing neural network C-language source code

聚类分析简介，是个课件，希望对大家有帮助。-Introduction Cluster analysis is a courseware, we want to help.

基于MATLAB完成的神经网络源程序 大家-Based on the MATLAB neural network to complete the U.S. source

Matlab fuzzy control progrom.

人工神经网络- Artificial nerve network

程序包含的设置包括：列数、样本总个数、建模样本数、预报因子数、预报对象数、学习因子、 动量因子、训练次数、总体误差、隐节点数。-procedures contained in the set include : number, the total sample number, the number of samples modeling, forecasting several factors, forecasting several targets, learning factor, momentum factor, the number of training, the overall error of hidden nodes.