在matlab 环境下用遗传算法求解TSP问题。包含源代码和说明文档...

模糊控制与PID控制比较,模糊控制仿真的阶跃响应-fuzzy control

pso 粒子智能优化算法，C语言实现。欢迎下载。-Intelligent Optimization Algorithm pso particles, C language. Welcome to download

（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

用VC++实现的，超级好用的8种神经网络源代码,超好用,超简洁 ，是我多年研究所得，已经全部测试过-With VC++ Realize, and eight kinds of super-to-use neural network source code,超好用, super-simple, is my research for many years, they have all been tested

神经网络算法源程序，如ADALINE，提供针对算法的演示源程序-neural network algorithm source, such as ADALINE provide demonstration against the algorithm source code

用C语言实现的BP网络的程序,严格按照BP的算法流程,开发环境为WIN-TC-C language of BP procedures in strict accordance with the BP algorithm. Development Environment for the WIN-TC

遗传算法的源程序例子，对于初学者有很大帮助，建议多-Genetic algorithm source code examples are very helpful for beginners, the proposed multi-

加法难题 有如下的加法算式： donald + gerald = robert 每个字母代表一个不同的数字(0-9)，你能否求出每个字母分别代表什么数字？-Adder problems with the additive formula is as follows : donald gerald = robert each letter representing a different figures (0-9), Can you calculated each letter on behalf of what?

基于matlab的小波分析工具箱WAVELAB850-based on the wavelet analysis Matlab toolbox WAVELAB850

详细介绍了SVM 和RVM的区别及各自的优点，说明RVM在很多方面优于SVM-Described in detail the difference between SVM and the RVM and their respective advantages, indicating RVM in many ways superior to SVM