right documentation can be achieved using DES encryption algorithm

条码技术-barcode technology

fortran版 神经网络遗传算法-fortran version of neural network genetic algorithm

matlab所有的工具箱，包括统计工具箱、拟合工具箱、微分方程工具箱、遗传算法工具箱、模拟退火工具箱、优化工具箱。随机数工具箱、等等。很全面-All matlab toolbox, including statistical toolbox, fitting toolbox, toolbox differential equations, genetic algorithm toolbox, simulated annealing Toolbox, Optimization Toolbox. Random number toolbox, and so on. Very comprehensive

NSGA-II遗传算法的文档，里面的内容比较好理解，是学习遗传算法的必备知识-Genetic Algorithm NSGA-II of the document, which is better understanding of the content is a genetic algorithm to learn the knowledge necessary

本程序实现通过构造一棵前缀树实现 最大模式频繁项集挖掘算法.应用fp树增长算法实现.-the program by constructing a prefix tree to achieve the greatest model Frequent Mining algorithms. Application fp growth tree algorithm.

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

这是用C语言编写的神经网络源程序，各种网络都有相应的C程序-This is prepared by using C language source code neural networks, a variety of network have corresponding C procedures

可拓学的专家系统,基于B/S结构,动态,网络专家系统.-Extenics expert system, based on B/S structure, dynamics, network expert system.

介绍了神经 网络中的BPN算法的C语言实现.-Introduce a neural network BPN algorithm C language.

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