遗传算法工具箱matlab

开发环境:Matlab 简要说明:自组织特征映射模型(Self-Organizing feature Map)，认为一个神经网络接受外界输入模式时，将会分为不同的区域，各区域对输入模式具有不同的响应特征，同时这一过程是自动完成的。各神经元的连接权值具有一定的分布。最邻近的神经元互相刺激，而较远的神经元则相互抑制，更远一些的则具有较弱的刺激作用。自组织特征映射法是一种无教师的聚类方法。-development environment : Matlab Brief Description : Self-Organizing Map model (Self-Organizing Map feature), a neural network that external input mode, will be divided into different regions, the regional input to the model with different response characteristics and the process is automatic End %. The neurons connect with the right to a certain value of the distribution. Most neighboring neurons stimulate each other, distant neurons were mutual inhibition, the vision has a weaker stimulus. Self-organizing feature mapping method is a non-teachers clustering method.

MATLAB神经网络工具箱ＢＰ算法的比较，比较它们的训练时间长短以所达到的效果-MATLAB neural network toolbox BP algorithm compares the length of their training time in order to achieve the effect of the so

bp神经网络识别数字，可识别0-9的十个数字-the number of bp neural network can identify the ten figures 0-9

GEP（基因表达式程序设计）是一种新的演化算法，该文件是基本的GEP编程，采用功能的封装格式，测试通过，能够方便的使用。-GEP (Gene Expression Program Design) is a new evolutionary algorithm, which is the basic GEP programming, functional package format, through testing, to facilitate their use.

新手上路： 自己编写的BP神经网络程序，还不是成熟，希望能够加入社区和高手们学习学习-Getting Started: I have written procedures for the BP neural network, is not yet mature, I hope you can join the community and learning to learn

一个比较简单的遗传算法程序，有注释，适合初学者一般学习

用遗传算法进行排序-genetic sequencing algorithms

一个遗传算法（GA）的实现。command line 的结构。输入输出格式请见命令行-A genetic algorithm (GA) implementations. command line structure. Input and output format, see the command line

自己汇总了几个关于rbf的源程序，用于初学者学习-Own summary of a few on the rbf source for beginners to learn

利用遗传算法求最小值，程序中求得是表达式x1*x1+x2*x2+x3*x3再-2~2上的最小值，以及对应的x值，算法中使用二进制编码，交叉采用不同交叉和优势交叉，变异也分两种，不用变异和优势变异-using genetic algorithms for the minimum, procedures to seek expressions x1 x1 x2*** x2 x3 x3 another 2 ~ 2 on the minimum, and the corresponding value of x, algorithm using binary encoding, using different cross-cross and advantages of crossover and mutation at the two without variation and variation advantage