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这是一个研究人工智能中蚂蚁算法的研究性文档,对想了解这方面内容的人相当有益,很难才找到的-This is a study artificial intelligence ant algorithm research documents, to think about it as a very useful one, it is difficult to find the

人工智能领域最基本的问题-artificial intelligence fundamental problem

隐马尔科夫模型工具箱最新版本，可以广泛应用于识别等领域-HMM Toolbox latest version can be widely applied in areas such as identification

这个程序主要提高了神经网络，遗传算法，聚类等多种人工智能算法，采样c++编程，，很精简-This process mainly to improve the neural networks, genetic algorithms, clustering and many other artificial intelligence algorithms, sampling c++ Programmed, it is streamlined

遗传算法工具箱C++，本为Linux系统设计，现在改为在VC下能顺利编译而不需通过任何修改。基于面向对象方法设计，便于使用和扩展。-Genetic Algorithm Toolbox C++, This for the Linux system design, is now in the VC under the smooth adoption of the compiler without any changes. Method based on object-oriented design, easy to use and expand.

《遗传算法――理论、应用与软件实现》王小平曹立明著一书的参考光盘.rar-"genetic algorithm-- the theory, application and software," Wang Xiaoping Cao Liming with a reference to the CD. Rar

人工智能§神经网络模拟软件§java环境开发-neural network artificial intelligence java simulation software development environment

该算法为基于控制规则表的模糊PID控制 用MATlab开发，能够管直接运行-The algorithm for the control rules based on fuzzy PID control table with Matlab development, to pipe directly run

神经网络算法原程序内容较详细。希望大家仔细讨论这个问题。-neural network algorithm original program content in more detail. We hope to discuss this issue carefully.

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