本程序用于训练RBF神经网络权值的量子粒子群优化…

基于遗传算法的人工生命模拟源程序!-based on genetic algorithm source artificial life simulation!

VC写的harris角点检测程序。又名plessey，最经典也是目前效果最好的算法，比susan抗噪要好（实际的讲）-A program of Harris corner points test writen by VC.We can call is as plessey,it is the best algorithm.It better than susan from real useful point.

改进遗传算法－郭涛算法做最优化问题很管用,算法的基本思想是 先任意产生n个随机数，然后从n个数里随机选择m个数，再有这m个 数合成一个新数，将这个新数同n个数中间适应值函数值的最差的比较， 如果好的话就取代最差的那个，如果它比最好的还要好的话，则把最好的 也取代。如果比最差的坏，则重新合成一个新数。依次循环下去。 程序的奇妙之处是GA_crossover()函数，产生的新数确实比较好，看看 那位大侠能改进一下，产生比这跟好的数。-improved genetic algorithm- Guo Tao done optimization algorithm is very effective, and the algorithm is the first basic idea arbitrary n generated random number and then the number li n m randomly selected number, and this number m of a new synthesis of the new middle with n number fitness function of the worst by comparison, if a good case to replace the worst, um, if it even than the best, then ruled the best have replaced. If worse than the worst, a de novo synthesis of new. Followed by the cycle continues. The magic of procedures is GA_crossover () function, the new really good to see heroes who can be improved upon, it generated more than a few with a good.

％ RBF 神经网络用于模式分类 ％ 使用平台 - Matlab6.5- RBF neural network for pattern classification use of the platform- Matlab6.5

关于神经网络方面的130个程序，采用matlab语言，比较全-On the neural network 130 procedures, the use of matlab language, compare the whole

一份中科院开发的语义分析源码，可编译运行。里面的代码都很完整，注释也清晰，是研究语义语法分析的必不可少的学习工程。-A semantic analysis of source code developed by Chinese Academy of Sciences, can be compiled to run. The code inside the very integrity of annotations is also clear, is to study the semantic parsing essential to study engineering.

神经网络的开发包，采用c++编写的，使网络资源，大家共享-neural network development kits, prepared by the use of c, network resources and share

Hopfield Model Autoassociative Memory 源码, 经典的HOPFIELD人工神经网络例子源码-Hopfield Model Autoassociative Memory source, HOPFIELD classic example of artificial neural network source

tsp问题，是一个很典型的NP完全问题，该程序用了遗传―退火结合算法，能优化计算-tsp problem is a typical NP-complete problem, the program is to use genetic- combined annealing algorithms that optimize computing

遗传算法，包含选择，交叉，变异等操作，可求出Y=sin(x)在0-2π的最大值和最小值-Genetic algorithm, including selection, crossover and mutation operation, etc., can be obtained Y = sin (x) at the 0-2π Maximum and minimum