本人调试的大作业（源程序模型+论文），主要包括PID控制、模糊控制、神经网络控制、遗传算法优化神经网络控制（使用了遗传工具箱GAOT）对同一系统所作的仿真比较，

蚁群算法的实现，使用一个例子验证蚁群算法的有效性。-ant algorithm implementation, use an example to verify the validity of ant colony algorithm.

这是我自己花了很多时间写的一个模式识别源码。奉献出来与感兴趣的朋友分享，绝对可靠。.. - Dedication out with friends interested in sharing, absolutely reliable. ..

国外的一个A*算法演示，带源代码。作为算法参考非常好-abroad an A* algorithm demonstration, with the source code. As a very good reference algorithm

遗传算法的文章- Heredity algorithm article

可用于bp,elman,rbf,grnn四种神经网络的预测命令-Can be used for bp, elman, rbf, grnn four types of neural network prediction order

自己写的一个简单的神经网络(BP)算法，用来解决异或（XOR)问题,初学神经网络的人可以下来看！-Wrote it myself a simple neural network (BP) algorithm, to solve the exclusive-OR (XOR) problem, learning neural network can look at the next!

遗传算法的讲义！- Heredity algorithm printed lecture!

AIML的实现，AIML是机器人语言中比较适用的原型，希望大家喜欢-AIML realization, AIML robot language is more applicable to the prototype, I hope everyone likes

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

一个DoS下的神经网络程序.-the neural network programming.