加了动量项和域值，比较好用的bp网络算法

这是一个用vb写的TS算法，可以提供初学者尝试

分类器的设计，包括knn方法和BP的方法。对一系列样本点进行分类-Classifier design, including methods and BP KNN method. On a series of sample points to classify

神经网络相关算法，使用java语言进行描述的-Neural network-related algorithms, using the java language to describe the

文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层 到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索 路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法 无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum.

模式识别―分类程序VC源码,是《图像模式识别VC++技术实现》一书的源码-pattern recognition-classification procedure VC source, "VC image pattern recognition technology to achieve" a source book

本程序实现了多城市的tsp问题（即旅行商问题）的遗传算法-the process of multi-city tsp (ie, the traveling salesman problem) Genetic Algorithm

神经网络BP算法-neural network algorithm

炉温模糊控制的一般性程序，可以修改各参数从而更改模糊控制表-Temperature control of the general procedures can be amended so as to change the parameters of fuzzy control Table

人工智能中经典算法-宽度搜索和启发是搜索（A*算法）在VC环境下的实现代码，对于学习人工智能有很多帮助- In the artificial intelligence the classical algorithm- width search and the inspiration are the search (the A* algorithm) under the VC environment realization code, has regarding the study artificial intelligence very helps

用parzen窗方法，估计概率密度，采用高期核函数-With parzen window means of estimating the probability density function using high nucleus. . . .