模式识别中关于感知器算法的程序,输入点进行聚类.

本例是本人写的遗传算法的最简单的一个实现，并且带有一个说明文档。用C语言编写，在TurborC中调试，由于当时在随机数产生上没有种种子，所以极易收敛，有兴趣可以种上种子试一下。-the cases I wrote is the most genetic algorithm to achieve a simple, and with a documented. C language, in which TurborC debugging, because the random number generator not all sons, so vulnerable convergence are interested can plant seeds try.

用VC++编写的基本遗传算法的源程序，而且有中文解释，适于初学者。-GA

一个改进的聚类算法FCMA,可以实现数据的自适应有效聚类,对2维数据测试效果较好- An improvement gathers kind of algorithm FCMA, may auto-adapted realize the data effectively to gather the kind, is better to 2 dimensions according to the test effect

水平有限，仅供参考！望大家多提宝贵意见！-limited, and for reference purposes only! Hope you will provide more valuable opinions!

用vc开发的A*寻路算法的dll,可以在其他语言调用，包含了一个vb调用的示例-The dll file of A* road-mapping algorathm,which can be used in other language,including an example of vb.

利用最大匹配法进行汉语句子的分词 最大匹配算法是最常用的分词算法，简单实用正确率可达到80%以上-the maximum matching method for the Chinese Sentence Word maximum matching algorithm is the most commonly used word segmentation algorithm, simple and practical accuracy rate can reach more than 80%

遗传算法做图像的模式匹配，绝对经典，有源码看，有执行文件可以执行，下面有个执行环境的文件请先安装。-Genetic algorithm to do pattern matching images, absolute classics, and have look source, has the implementation of a document can be implemented, has the following months the implementation of environmental documents, please install.

张文修老师主编的详细介绍遗传算法及其数学基础的一本书，研究遗传算法的一本很好的参考书，特别推荐！-Edited by Prof. Zhang Wenxiu, detailed genetic algorithm and its mathematical foundations, a very good reference book to study the genetic algorithm, strongly recommended!

开发环境：Visual C++ .net2003 功能介绍：神经网络算法实验；分Console版本和MFC版本；主要用来求解TSP问题。-Development Environment: Visual C++. Net2003 function introduction: neural network algorithm experiments sub-Console version and MFC version mainly used to solve the TSP problem.

数据挖掘算法之一，基于代表点的CURE聚类算法，该算法先把每个数据点看成一类，然后合并距离最近的类，直至类个数为所要求的个数为止。-CURE cluster algorithm based on representive point,one of data mining algorithms,classifies each data as a category firstly, then unifies categories with the nearest distance into one until the number of class is coincidence with the classes demanded.