专家浅谈人工智能，doc文档

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

遗传算法的通用程序，可以根据需要改编以适应不同的应用-Generic genetic algorithm procedure can be adapted to the needs of different applications

本代码用蚁群算法求解带时间窗的车辆路径问题-The code with ant colony algorithm with time window of vehicle routing problem

基于ELMAN神经网络对阶跃响应动态进行补偿也可以举一反三的运用于ELMAN网络的其他应用-Elman neural network based on the step response of the dynamic compensation can be applied to an example of the Elman network other applications

遗传算法强制交叉算法-GA mandatory cross-Algorithm

该程序包主要讲述了遗传算法。文中的遗传算法是由C语言所编出来的。能够达到很好的优化效果。-The package focuses on the genetic algorithm. In this paper, genetic algorithm is made by the C language out. Optimized to achieve good effect.

股票交易系统自动根据指标进行相关的交易系统-Automatic stock trading system in accordance with indicators related to the trading system

标准遗传算法代码 simple genetic algorithms-simple genetic algorithms

提出了一种利用S函数实验结果表明：ICA可以将 脑电信号中包含的心电（ECG）、眼电（EOG）等多种干扰信号成功地分离出来-use of a S-function experimental results show : ICA EEG can be included in the heart (ECG), eyes (EOG) and other interference signal successfully separated

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and ot