AI 人工智能 在C#环境下实现 9宫格 算法

一个简单实用的K紧邻分类器 -KNN

简单的遗传算法的例子,对于遗传算法的初学者来说非常有用.-Simple genetic algorithm examples.it very helpful for the beginners of genetic algorithm

好不容易找到的外国博士论文,关于Text-independent speaker recognition-Hard to find foreign doctoral thesis on the Text-independent speaker recognition

人工智能BP问题的C语言实现，BP算法可见人工智能教材-the C language, BP artificial intelligence algorithms can be seen teaching

该系统使用极其简便，即使 你对各种网络模型不是很深刻的了解，也可以很好的使用该系统。使用时， 你可以自己修改网络的各种参数，交互性较好，而且该系统通过大量的图示 及参数设置，可以让你了解每个应用实例实现的过程及详细步骤。-The system is extremely easy to use, even if you have to various network model is not very profound understanding can be a good use of the system. When used, you can change the network"s own parameters, interactive better, but the system through a lot of icon and parameter settings, so you can understand each application process and the realization of detailed steps.

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.

K-Means动态聚类算法源程序 使用K-Means实现数据挖掘中的聚类算法-Dynamic K-Means clustering algorithm source realize the use of K-Means clustering data mining algorithm

SVM是一种优秀的分类算法，常用于用于回归分析和分类，而matlab语言的libSVM则为广大使用matlab进行设计的人员提供很好的帮助。-libSVM for regression classification

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

人工智能搜索法解决九宫重排问题（又称8数码问题）。-artificial intelligence search JiuGongTu rearrangement solve the problem (also known as the Digital 8).