演化程序――遗传算法和数据编码的结合 具体介绍了遗传算法的应用...

cost存放了一个强连通图的边权矩阵，作为一个实例。 可在workspace中加载 运用此算法要注意多次试验。 bianquan.m文件给出了一个参数实例，可在命令窗口中输入bianquan,得到邻接矩阵C和节点个数N以及一个任意给出的路径R,，回车后再输入jiaohuan3,得到了最优解。 由于没有经过大量的实验，又是近似算法，对于网络比较复杂的情况，可以尝试多运行几次jiaohuan3,看是否能到进一步的优化结果。-storage cost of a strongly connected graph edge weight matrix, as an example. Can be loaded in the workspace to use this algorithm to the attention of numerous tests. bianquan.m document gives examples of a parameter can be in the command window, type bianquan, be the adjacency matrix C and the node number N and an arbitrary given path R,, carriage return after the input jiaohuan3, have been the optimal solution . After the absence of a large number of experiments, it is approximate algorithm, for more complex network situations, you can try to run several jiaohuan3, can look to further optimize the results.

vc++ 实现的人工神经网络bp网络的源代码-bp Algorithm

模拟退火模型算法，使用C语言实现的，简单有效-Simulated annealing algorithm, the use of C language to achieve, simple and effective

输入一个任意大小的迷宫， 用栈求出一条走出迷宫的路径， 并显示在屏幕上。-importation of a maze of arbitrary size, with a stack get out of the maze of trails and on the screen.

apriori算法是数据挖掘的经典算法,它基于关联规则的思想.此为我的第3个收藏-algorithm data mining algorithm is the classic algorithm, which is based on the idea of association rules. This was my first three collections

用RBF实现的目标图象识别程序,C实现,可以应用于DSP系统.-RBF realize the goal of using the image recognition program, C realize, can be used in DSP systems.

蚁群算法解决0-1背包问题,是一个比较小的程序，运行过，没有问题的。-the solving method of zero to one backage with ACO.It is a small programe for the zero to one problem.

BP人工神经网络算法，用java代码实现，用scilab图像显示，案例是港口吞吐量的预测-BP artificial neural network algorithm, using java code, using scilab image display, case is the throughput of the port prediction

这是一个二值hopfield神经网络源程序，有一个实例，训练数据保存在h7x8n4.trn,输出数据保存在ARCHIVE.LST中。-This is a value hopfield two neural network source, an example of training data stored in h7x8n4.trn, output data stored in ARCHIVE.LST China.

基于神经网络的电离层F2层临界频率预测方法 基于神经网络的电离层F2层临界频率预测方法-Based on neural network of ionospheric layer F2 critical frequency forecasting methods based on neural network of ionospheric layer F2 critical frequency forecasting methods