这是用VC实现的模糊控制算法，稍作处理可以达到自己的要求

一个使用Java实现的人工智能的图搜索策略程序――酒九宫图问题 人工智能－图搜索策略－演示程序－使用说明 程序简介 在人工智能领域，对隐式图的搜索是求解问题的一种基本方法，本程序通过使用多种图搜索策略，对 重排九宫问题进行求解，演示了“广度优先搜索”、“深度优先搜索”、“有界深度优先搜索”、 “最好优先搜索”和“局部择优搜索”五种基本的图图搜索策略。 整个程序使用了动画技术，界面设计美观友好，使用方便。-use a Java artificial intelligence plans Search Strategy-- Liquor JiuGongTu problems of artificial intelligence-map search strategy- Demonstration Program-use guide in the field of artificial intelligence, the implicit search of the plan to solve the problem is one of the basic methods, the procedures through the use of multiple map search strategy, the rearrangement JiuGongTu problem solving, presentation of the "breadth- first search," "depth-first search", "bounded depth-first search", "best-first search" and "partial selection of the best search" five basic map search strategy. The whole process of using animation, interface design aesthetic friendly, easy to use.

程序代码说明 神经网络的实例,内附详细说明-Code description examples of neural network, containing a detailed description of

用于程序优化与遗传算法-procedures for optimization and genetic algorithms

weka机器学习系统（本站可下载）的拓展，简化了从关系数据库中提取数据-weka machine learning system (download site), the development of a simplified relational database from which to extract data

分类算法的研究很详细 很权威的 值得看看 有研究意义-Classification algorithm in detail to see if there should be a very authoritative research significance

这是一篇步态识别和检测的文章：这是在视频流的基础上，区别于以往的静态图像-This is a gait recognition and detection of the article : This is the video stream on the basis of distinguished from the previous static images

C#版遗传算法解TSP问题-C# version Genetic Algorithm for the Traveling Salesman Problem

apriori算法是数据挖据中的经典关联规则挖掘算法，是由aggwal最早提出来的-apriori algorithm is data-dug, according to the classical association rule mining algorithm is put forward as early as aggwal

最大概率分词法,这种分词算法能够较好的解决汉语分词中的歧义问题,但分词效率比最大匹配分词算法要低-greatest probability points accidence, Segmentation algorithm can be used to solve the Chinese word segmentation of Ambiguity, but Word efficient than the largest matching segmentation algorithm lower

围绕粒子群的当前质心对粒子群重新初始化.这样，每个粒子在随后的迭代中将在新的位置带着粒子在上次搜索中获得的“运动惯性”（wvi）向Pi，Pg的方向前进，从而可以在粒子群的运动过程中获得新的位置，增加求得更优解的机会.随着迭代的继续，经过变异的粒子群又将趋向于同一点，当粒子群收敛到一定程度时又进行下一次变异，如此反复，直到迭代结束.-particle swarm around the center of mass of the current PSO reinitialization. Thus, Each particle in the next iteration will be in the new location with particles in the last search was the "inertia" (wvi ) Pi, Pg orientation, and thus can PSO course of the campaign was a new position, increase seek better solutions opportunities. With the continued iteration, after variation of PSO will tend to the same point. When PSO converge to a certain extent when the next variation, so repeatedly, until the end of iteration.