计算前提论域中模糊子集的C语言程序.大家可以参考。

清华大学不确定规划教材中的用遗传算法训练神经元网络的混合智能算法1-Tsinghua University, uncertainty in the planning of teaching materials by using genetic algorithm trained neural network Hybrid Intelligent Algorithm 1

这是一个模式识别中的最近邻分类算法的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition algorithm for nearest neighbor classification simulation of a simple classification example, is one of female.txt and male.txt training samples, test.txt is the measurement, classification effect is very good for beginners will be learning model there is a great help.

这个是神经网络中的HopField网络模拟，是利用外向和的双极性HopField网络，有很好的借鉴价值。-This is a neural network of Discharge network simulation is the use of the outward and bipolar Discharge network, a very good reference value.

神经网络C源程序（nnbp)-neural network C source (nnbp)

数据挖掘中k-means聚类算法的实现源代码-clustering algorithm source code in data mining

人脸检测代码示例系统.rar 希望对大家有所启发。-Face Detection System code examples. Rar we hope to inspire.

Schapire 所著的Boosting算法的教程。 对目前物体识别和机器学习领域中广泛应用的Boosting算法展开了深入浅出的描述，还有很多Toy Examples。-Tutorial for Boosting algorithm by Schapire

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 ...

遗传算法求解最优解求函数的最大值 : f(x,y)=21.5+x*sin(4*PI*x)+y*sin(20*PI*y) | 定义域 D: -3

矩阵计算库，经改编过的，可以扩充，可读性很好。-matrix calculation library, adapted too, can be expanded, good readability.