支持向量机

一个用C编写的SOM（自组织特征映射）神经网络源代码。-a C prepared by the SOM (self-organizing feature map) neural network source code.

（其中已经包含5个源码）所附源程序C或C++代码文件及其可执行文件 Scs.cpp 基本分类算法源程序， 输入数据文件cfile.txt,efile.txt,gfile.txt,pfile.txt ,rfile.txt,tfile.txt Sga.c 基本遗传算法源程序, 输入数据文件input，输出文件output A_life.c 基于遗传算法的人工生命模拟源程序, 输入数据文件world GA_nn.c 基于遗传算法优化神经网络结构源程序,输入数据文件sample Patmat.c 基于遗传算法提取基元图形源程序-(which includes five OSS) attached to C or C source code files and executable files Scs.c pp basic classification algorithm source files, input data files cfile.txt, efile.txt. gfile.txt, pfile.txt, rfile.txt. tfile.txt Sga.c basic genetic algorithm source files, input data files input, output file output A_life.c based on genetic algorithm simulation source Artificial Life, import data files GA_nn.c world based on genetic algorithm optimization neural network structure source, import data files Patmat.c sample extraction based on genetic algorithm-based graphic source yuan

这是一本粗糙集的数据挖掘经典书籍，书中不但介绍了粗糙集的相关知识，并给出了相关应用，同时介绍并比较了其他挖掘方法。-This is a rough set data mining classic books, the book not only introduced a rough set of relevant knowledge, and gives the related application, at the same time and compare the introduction of other mining methods.

介绍了神经 网络中的CPN算法的C语言实现.-Introduce a neural network algorithm in C language CPN realize.

用粒子群方法解决TSP（旅行商）问题，按照TSP1.m中的格式输入程序参数即可-Using Particle Swarm solution to TSP (traveling salesman) problems, in accordance with the format in TSP1.m parameter input program

本程序是关于qam的解调程序用c语言编写希望大家能用-this process is on the demodulation process qam c language with hope everyone can be prepared

数据挖掘c45分类算法 数据挖掘c45分类算法-C45 data mining data mining classification algorithms C45 classification algorithm

利用遗传算法优化BP神经网络 即用遗传算法代替BP网络中的训练函数-The use of genetic algorithm to optimize BP neural network using genetic algorithms in place of BP network training function

神经网络中学习矢量量化模型算法的源程序，属于有导师学习！-Neural network model of learning vector quantization algorithm source code, which has instructors to learn!

求最优值的一个算法-seeking an optimal value of the algorithm