fp_growth，apriori算法的升级版，减少了扫描数据库的次数

移动机器人运动学mathematica程序-Mobile robot kinematics mathematica program

以某区水文数据为例，在matlab7中构建BP神经网络，进行仿真预测。-Hydrological data in a certain district, for example, in matlab7 Building BP neural networks, simulation prediction.

人工神经网络matlab编程，BP网络实例 入境旅游人数的预测结果对于旅游管理部门和政府部门有着重要的参考意义,寻求科学合理的预测模型是保障预测结果准确可靠的关键。根据我国入境旅游人数的实际情况,建立由一个输入层、一个隐含层、一个输出层构成的三层BP 网络模型-ANN Matlab programming, BP examples tourist arrivals in the forecast results for the tourism management and government sectors have important reference value, search for a scientific and rational prediction model is to ensure accurate and reliable prediction of the key. According to China"s tourist arrivals in the actual situation, and establish an input layer, a hidden layer, an output layer consisting of a three-tier network model BP

这是一个将n个样本聚类到m个类别中 将n个样本在m个类里如何分配输出的遗传算法代码。 适应度：sum(i=1_110)sum(j=1_20)【xi-vj】~2-This is a cluster n samples to the m categories will be n samples in the m a class how to allocate the output of the genetic algorithm code. Fitness: sum (i = 1_110) sum (j = 1_20) [xi-vj] ~ 2

这是用matlab编写的用遗传算法优化神经网络的程序-using Matlab is prepared by using genetic algorithm optimization procedures for neural networks

机器学习讲义机器学习很好的材料内容很全面-Machine Learning Machine Learning Materials Materials is very good all-round

完全图哈密尔顿圈的遗传模拟退火算法matlab通用源程序-Complete graph of the Hamilton Circle genetic simulated annealing algorithm matlab source generic

一个基本遗传算法求函数最大值的程序，在Vc中运行结果很好，有详细的代码注释，可以做为模板使用。 sag_sag.c-a basic function of genetic algorithms for maximum procedures, the Vc running good results, a detailed source notes, can be used as a template. Sag_sag.c

bp人工神经网络算法实现，用java编写程序代码，应用案例是港口吞吐量预测-bp artificial neural network algorithm, using java programming code, the application case is the throughput of the port prediction

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 ...