遗传算法神经网络，遗传算法神经网络遗传算法神经网络

基于基本遗传算法的函数最优化，C++源代码-Price quilt hanging遗price for dialysis tub cavity算Finally, Yang Zhao ， Pro C++ Daitou Tungsten measurements

这是一本粗糙集的数据挖掘经典书籍，书中不但介绍了粗糙集的相关知识，并给出了相关应用，同时介绍并比较了其他挖掘方法。-This is a rough set data mining classic books, the book not only introduced a rough set of relevant knowledge, and gives the related application, at the same time and compare the introduction of other mining methods.

《游戏编程中的人工智能技术》一书中10，11章源代码- Game programming in artificial intelligence technology, one source book, Chapter 10,11

AIML的实现，AIML是机器人语言中比较适用的原型，希望大家喜欢-AIML realization, AIML robot language is more applicable to the prototype, I hope everyone likes

Kohonen网络的学习过程可描述为：对于每一个网络的输入，只调整一部分权值，使权向量更接近或更偏离输入矢量，这一调整过程就是竞争学习。随着不断的学习过程，所有输入矢量都在输入矢量空间相互分离，形成了各自代表输入空间的一类模式，这就是Kohonen网络的特征自动识别的聚类功能。请解压缩后按照readme提示进行操作。-Kohonen network learning process can be described as follows : for each one network input, only part of adjusting the weights, weight vector closer to or further from the input vector, the adjustment process is competitive learning. With the continuous process of learning, all the input vector in the importation of vector space separate form their own representatives importation of a type of space, and this is a Kohonen network of automatic clustering functions. Please decompress after readme according to instructions.

径向基函数网络应用实例-RBF Network Application

黑白棋子的算法程序实现详细步骤及实现过程-reversi son algorithm program and detailed steps for achieving

LS-SVM_UserGuide是LS-SVM使用方法介绍的一篇文章，竟对我们学习支持向量机有很大的帮助。-LS-SVM_UserGuide is the use of LS-SVM method described in an article we had on the support vector machine learning of great help.

DBSCAN是一种性能优越的基于密度的空间聚类算法．利用基于密度的聚类概念，用户只需输入一个参数，DBSCAN算法就能够发现任意形状的类，并可以有效地处理噪声．-DBSCAN is a superior performance of space-based density clustering algorithm. The use of the concept of density-based clustering, the user can enter a parameter, DBSCAN algorithm to be able to find any type of shape, and can effectively deal with noise.

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,