基于遗传算法和神经网络的外星人游戏的vc＋＋实现的程序

用c语言实现的bp神经网络实现异或功能，比较好用 -use the C Language bp neural network XOR functions, better quality

模糊算法，实现了求交法计算模糊蕴含关系、最大最小法进行合成运算、求并法计算输出量的模糊集合、加权平均法计算实际的清晰控制量等部分的详细代码。-Fuzzy algorithm, the realization of the intersection method to calculate fuzzy implication relations, max-min method for synthesis of computing, and method for output fuzzy sets, the weighted average method to calculate the actual volume of clear control of part of the detailed code.

Apriori算法的JAVA实现A priori算法的JAVA实现-Apriori algorithm JAVA realize A priori algorithm realize JAVA

多层神经网络范例 http://www.codeproject.com/cpp/MLP.asp?df=100&forumid=148477&exp=0&select=1141594#xx1141594-multilayer neural network model http :// www.codeproject.com/cpp/MLP.asp df = 1

遗传算法神经网络，遗传算法神经网络遗传算法神经网络-genetic algorithm neural networks, genetic algorithms neural networks, genetic algorithm, neural network

--- -一个介绍支持向量机的文档。 认真读一读，会有收获的。-------- introduced a Support Vector Machine documents. Seriously read, there will be rewarding.

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

基于VC++的小波提升程序，包含小波提升的详细算法。对初学者有很大帮助-Based on VC++ The Wavelet Lifting procedures, including detailed algorithm Wavelet Lifting. Have a great help for beginners

求图中的祖先节点(不含父亲节点) 内含说明，学图论或人工智能的朋友会用到的。希望有利于理解书上的论述。-plans for their ancestral nodes (excluding the father nodes) intron show that studying graph theory in artificial intelligence or a friend will use the. Hope to understand book expositions.

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