机器人足球的程序,机器人足球大赛的有关源代码

基因表达式编程 基本算法 能够完成术用于公式发现、函数挖掘，关联规,则挖掘，因子分解，和预测等-gene expression programming algorithm can be used for the completion of the formula found that function Mining, related regulations, mining, factorization, and the forecast

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

一个运用改良的遗传算法来解决旅行商问题的程序-an improved use of genetic algorithms to solve the traveling salesman problem procedures

this about the artificial neural networks which uses the back propogation method.-this is about the artificial neural networks which uses the back propogation method.

计算机网络经典算法：生产者与消费者问题的源代码-classic algorithm of computer network:producer and consumer

销售预测系统，可以根据历史销售数据对未来的销售量进行有效预测，采用BP神经网络对预测模型进行训练，可以达到不错效果-sales forecasting system, based on historical sales data for the future effective sales forecasts, BP neural network model to predict the training, they can achieve good results

遗传算法，包含选择，交叉，变异等操作，可求出Y=sin(x)在0-2π的最大值和最小值-Genetic algorithm, including selection, crossover and mutation operation, etc., can be obtained Y = sin (x) at the 0-2π Maximum and minimum

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.

研究基于遗传编程的可持续性进化算法的理论与方法，并建立了该算法的数学模型及运算流程.HFC算法实现源代码.-Research based on genetic programming evolutionary algorithm for the sustainability of the theory and method, and set up a mathematical model of the algorithm and the computation flow. HFC algorithm source code implementation.

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.