Hopcroft_Karp

实现LSB数字图像水印算法，限于BMP图像(LSB digital image watermarking algorithm to achieve, limited to the BMP image)

source code of train function of fuzzy bp neural network

说明：  a beginner s guide to mathematica(a beginner' s guide to mathematica)

说明：  运用Matlab求解三个传递函数的群延迟特性曲线(Use Matlab to solve the three transfer function of the group delay characteristic curve)

说明：  这是一个用MATLAB写的db4小波程序，有主程序等(this is a function file write in matlab,embrace a main function )

基于小波包BP神经网络的短期电力系统负荷预测程序(Electric power system short-term load forecasting BP wavelet packet neural network program based on)

LTE with SUI channel

基于负熵最大的独立分量分析算法，可以将独立的混合信号分离(Based on the negative entropy of the largest independent component analysis algorithm can be a separate mixed-signal separation)

Converts a pointcloud (unordered vertices) to a mesh of triangles by connecting the nearest 3 points and calculates the normal of each vertex and triangle. Also finds the neighboring triangles of each vertex and the neighboring triangles of each triangle

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)