自适应共振理论（艺术）

在vc环境下的指纹识别与处理，我也是从别处得到的，主要看看算法结构。-vc environment in the fingerprint identification and handling, I also received from elsewhere, mainly look at the 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

基于基本遗传算法的函数最优化 ，这也是本人收集的遗传算法优化软件，望对本网站有所帮助！！ -based on the basic function of genetic algorithm optimization, which I gather the genetic algorithm optimization software, look on this site help! !

基于信息熵的免疫算法，解决TSP问题的，但是我也是初学，这个看不太明白，共享一下吧-Entropy-based immune algorithm to solve TSP problem, but I am also a beginner, do not quite understand this view, share yourself

Demonstration and test of the kernel-ica package

这是一个HOPFIELD神经网络程序，里面有MAKEFILE，方便读者运行-HOPFILED This is a neural network program, which has MAKEFILE easy operation

vb写的改进的遗传算法程序。是一些通用代码，与具体问题对应的代码空出来，用的时候填进去。根据我的经验，要想用好遗传算法，代码必须自己写，因为问题不同，编码，杂交，变异算子可能都不一样。特别是杂交算子，需要根据问题调整，甚至创造出新的杂交方法。而且适应度计算的代码也需要自己写，特别是带约束的优化问题 -vb write improvement in the genetic algorithm. Some common code, with the specific issues the corresponding code space, and the time spent filling into account. According to my experience, in order to make good use of genetic algorithms, code must write their own, because different issues, coding, hybridization and mutation operator may not like. In particular hybrid operator, the need to adjust the problem, or even create a new hybrid method. But fitness calculation of the code required to write their own, especially with the constrained optimization problem

对股票走势的仿真，基于agent的设计思路-The trend of the stock simulation, agent-based design ideas

Java遗传算法库-Java genetic algorithm library

MCRGSA------组播路由问题遗传模拟退火算法 ％M-----------遗传算法进化代数 ％N-----------种群规模，取偶数 ％Pm----------变异概率调节参数 ％K-----------同一温度下状态跳转次数 ％t0----------初始温度 ％alpha-------降温系数 ％beta--------浓度均衡系数 ％ROUTES------备选路径集 ％Num---------到各节点的备选路径数目 ％Cost--------费用邻接矩阵 ％Source------源节点标号 ％End---------目的节点标号组成的向量 ％MBR---------各代最优路径编码-MCRGSA------ Multicast Routing genetic simulated annealing% M----------- genetic algorithm algebra% N----------- population size, even take Pm----------% probability parameter variation%----------- same temperature K Jump under a number of state----------% t0 initial temperature cooling% alpha------- beta coefficient%-------- balanced concentration coefficient% ROUTES------ Alternative Path Set% Enable--------- nodes to the number of alternative paths%-------- Cost adjacent costs Matrix% Source------ source node labeling% End--------- destination node labeling Group Vector%% of the MBR--------- generations optimal path coding