这里是一个利用人工智能的八个数字源代码，并对..

模式识别的书，介绍的论文模式识别的书，介绍的论文-Pattern Recognition book on pattern recognition paper book, the thesis introduction

人工智能bp算法程序以及实验报告-bp artificial intelligence algorithms and experimental procedures report

粒子群算法工具箱，有DEMO程序，可以对16中常见函数测试，并附有神经元网络的训练算法-Particle swarm optimization toolbox, there are DEMO program, you can test for 16 common functions, together with the training algorithm for neural networks

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

人工神经网络网上发给我的BP-ANN Internet to my BP

Fisher线性判别分类器源代码，包括训练函数和测试函数。-Fisher Linear Discrimination (FLD) classifier source code, including training function and test function.

一个VC编写的神经网络算法程序，对学智能算法的朋友很有用。-a VC prepared by the neural network algorithm procedures, intelligent algorithm friends useful.

为遗传算法提供基类，该基类将评价函数值直接作为适合度，采用新个体直接替换老个体的整体再生法 -genetic algorithm to provide the base class, the base class will be evaluated as a direct function of fitness, Individual direct new replacing the old individual"s overall regeneration

基于GA的BP改进算法，用遗传算法对BP 神经网络算法进行改进试用过 里面有数据是可以使用的-GA-based improvement of BP algorithm, genetic algorithm with BP neural network algorithm tried there are data that can be used

改进遗传算法－郭涛算法做最优化问题很管用,算法的基本思想是 先任意产生n个随机数，然后从n个数里随机选择m个数，再有这m个 数合成一个新数，将这个新数同n个数中间适应值函数值的最差的比较， 如果好的话就取代最差的那个，如果它比最好的还要好的话，则把最好的 也取代。如果比最差的坏，则重新合成一个新数。依次循环下去。 程序的奇妙之处是GA_crossover()函数，产生的新数确实比较好，看看 那位大侠能改进一下，产生比这跟好的数。-improved genetic algorithm- Guo Tao done optimization algorithm is very effective, and the algorithm is the first basic idea arbitrary n generated random number and then the number li n m randomly selected number, and this number m of a new synthesis of the new middle with n number fitness function of the worst by comparison, if a good case to replace the worst, um, if it even than the best, then ruled the best have replaced. If worse than the worst, a de novo synthesis of new. Followed by the cycle continues. The magic of procedures is GA_crossover () function, the new really good to see heroes who can be improved upon, it generated more than a few with a good.