解非线性规划模型的遗传算法，用c++语言编写，注释完整，结构清晰...

我们解决八数码问题,广度优先搜索可能会导致内存不够用,采用启发式搜索,启发函数为f(x)=g(x)+h(x) g(x)为该结点不同于目标结点的个数,h(x)为该结点的深度,选择那f(x)结点最小的那个结点进行扩展, 引入了一个"扩展数组[4]"(因为扩展的结点最多只有4个),该数组保存着某个结点的0点向各个方向的扩展的结点对象,然后对该扩展数组进行分析,利用启发函数在进行递归扩展... -us solve eight digital, BFS may lead to insufficient memory, using heuristic search, heuristic function f (x) = g (x) h (x) g (x) for the target node is different from the number of nodes, h (x) for the nodes of depth, the choice between f (x) nodes that the smallest node expansion. the introduction of an "expanded array [4]" (because the expansion of the nodes at most four), The array maintains a node of 0:00 a.m. to expand in all directions in the target node, and then expanded array analysis, use heuristic function during recursive expansion ...

k中心点 编制和调试一个程序，它将用户输入的正规式转换为以状态图和矩阵形式表示的确定有穷自动机。 1．把正规式转换为NFA 2．将NFA确定化为DFA • ＃作为正规式的终止符 • 考虑复合正规式 • 开始状态号为0 -focal point for the preparation of k and debug a program, it will the user to enter the formal conversion to a state diagram and matrix forms express the determination of DFA. 1. The formal type is converted to NFA2. Will determine the NFA into a DFA

神经网络的源代码，关于字符识别的一些东西-Neural network source code, something about character recognition

蚁群算法源代码，一个老外的例子-ant colony algorithm source code and an example of a foreigner

bp_算法~在bp_.cpp文件里~-11 bp_.cpp algorithm to the document ~

经典算法clique的java实现-Classical algorithm clique of the java implementation! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

TSP双种群蚁群算法。还包括了“30城市TSP问题数据与最优解.mat”，“75城市TSP问题数据.mat”，和“442TSP问题数据与算法对比.mat”-TSP-Stocks ant colony algorithm. Also include "30 cities TSP data and the optimal solution. Mat, " "75 cities TSP data. mat, " and "442TSP problem with the algorithm for data comparison. mat "

本程序是用java语言编写的数据挖掘分类算法中的决策树分类方法c4.5程序代码-this procedure is used java language classification of data mining algorithms decision tree classification code Bank

这是一个国外的Hopfield神经网络，有实例，可修改！-Hopfield Networks

基于神经网络的乒乓球机器人乒乓球轨迹预测代码，神经网络的参数可能有待进一步改进-code of PingPong Robot trajectory predicting based on neural network