蚂蚁算法的VC代码源，模拟蚂蚁觅食的行走途径，解决最优路径问题。...

基于遗传算法的排课系统，可以实现多种课程的排列-based on genetic algorithm Course Scheduling System, can achieve a wide variety of courses with the

针对TSP问题，用类神经网路的方法去求解！-against TSP, and the neural network approach to solving!

apriori算法是数据挖掘的经典算法之1,其基于关联规则的思想.这是我的第2个收藏算法-algorithm is data mining algorithms of a classic algorithms, based on the idea of association rules. This is my first two collections Algorithm

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.

运用MATLAB语言编写的连续型霍普菲尔德网络-using MATLAB prepared by the continuous Hopfield network

这是用VC编程实现模糊控制来控制机器人的运动-The misty control imitate really

文件中包含某老师关于人工神经网络控制系统的PPT，较详细的介绍了人工神经网络及应用于控制的相关内容。-File contains a teacher on the artificial neural network control system of PPT, a more detailed introduction to the artificial neural network and applied to the control of relevant content.

自己用JAVA编的人工智能的A*算法的八数码程序-own use JAVA series of the artificial intelligence of the A* algorithm eight digital procedures

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 ,

This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox