介绍了在LabView环境下的神经网络算法

国内的一个A*算法演示，可以自己画地图，演示效果非常好-domestic A* algorithm for a demonstration, can you paint maps, the effect was very good demonstration

遗传算法在路由优化中的应用:多服务质量的最小路径的算法.-Genetic Algorithm in Routing Optimization: Multi-service quality of the minimum path algorithm.

八数码问题用A*算法实现 VC++6.0实现的八数码问题-eightnum

程序演示的是用简单遗传算法随机一个种群，然后根据所给的交叉率，变异率，世代数计算最大适应度所在的代数-Program demonstrates that the simple genetic algorithm with a random population, then according to the crossover rate, mutation rate, calculating the maximum number of generations where the fitness of algebraic

这是一个非常好用的蚂蚁算法(蚁群算法)模拟程序,很好用噢,快来试试吧-This a very user-friendly ant algorithm (ant colony algorithm) simulation program, easy to use Oh, Come and try!

加了动量项和域值，比较好用的bp网络算法-added value items and Victoria, better quality bp Network

matlab环境下，对puma560机器人的工作空间运动轨迹做了轨迹规划，得到轨迹云图-matlab environment, the robot work space puma560 trajectories make the trajectory planning, the trajectory cloud

机器学习讲义机器学习很好的材料内容很全面-Machine Learning Machine Learning Materials Materials is very good all-round

模式识别中的几个常用算法，包括ISODATA算法、K－均值算法、感知器算法、LMSE最小误差、贝耶斯分类。-pattern_recognition have some usual algorithm,including ISODATA algorithm,K-means algorithm,apperceive algorithm ,

LIBSVM源码。LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、 易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、 n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ） 等问题，提供了线性、多项式、径向基和S形函数四种常用的核函数供选择，可以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概率估计等。-LIBSVM source. LIBSVM is林智仁Taiwan University (Chih-Jen Lin) Dr. develop design a simple, easy to use, fast and effective generic SVM software package, can solve the classification problems (including the C-SVC, n- SVC), regression ( including e- SVR, n- SVR) as well as the distribution of estimates (one-class-SVM) and so on, provides a linear, polynomial, radial basis function and the S-shaped kernel function of four commonly used for selection, can effectively to solve a wide range of issues, cross-validation to choose the parameters of the imbalance in the weighted sample, multi-category probability estimation.