张文修老师主编的详细介绍遗传算法及其数学基础的一本书，研究遗传算法的一本很好的参考书，特别推荐！...

最短寻路算法，利用Astar算法实现，是arp游戏中的寻路模型，有图例-Find the shortest path algorithm, the use of Astar algorithm is arp routing game model, there is Legend

使用线性分类器进行分类，采用感知器算法中的“奖惩算法”，各提取3类中的前25个样本共75个作为学习样本。-The use of a linear classifier for classification, perceptron algorithm using the reward and punishment algorithm , the extraction of 3 categories of the top 25 a total of 75 samples as the learning samples.

Visual studio 2005,C#开发 具有人工智能，可采用人工下棋、电脑参谋、电脑下棋等模式参加互联网比赛。采用ClickOnce发 -Visual studio 2005, C# Development of artificial intelligence, to be used in artificial playing chess, computer staff, the computer model of chess and other games take part in the Internet. Using ClickOnce fat

神经网络中的感知机的学习算法的c语言程序源代码-neural network of Perceptron learning algorithm in C language source code

有关神经网络的源码-the neural network source

多段图的分析算法,找到最优路径,用VC编的-more of the map analysis algorithms to find the optimal path, the series with VC

神经网络BP算法源代码-BP neural network algorithm C sourcecode

该文档是清华大学的模式识别教程，该版本是网页版本，浏览非常方便，内容也非常详细，非常适合初学者，个人举得特别是支持向量机那一章描述很棒！-this document is from Tsinghua University,this is the html version,it is convenient to read, it is very suitable for the elementary readers,in my own opinion,the charter of svm is very detail and easily understandable.

基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer t

神经网络C程序的第3章-neural network C procedures for the Chapter 3