遗传神经网络

简单的遗传算法源代码，挺实用的，大家可以借鉴-Simple genetic algorithm source code, very useful, we can learn from

改程序实现UB算法，对Web日志文件进行分析，产生相似度矩阵-reform program UB algorithm, the Web log file analysis, the similarity matrix produced

C++ 基于神经网络 法的数字 识别代码-C++ Based on neural network digital identification code

一个语音信号端点检测的程序，算法可能需要根据具体的应用调整一些参数。请使用者自己调整，根据不同的噪声环境。-A voice signal endpoint detection procedure, the algorithm may need to adjust according to specific application parameters. Please adjust the user, according to different noise environments.

RBF程序，可实现函数拟合，本人已经亲自调试过，效果不错。-RBF procedures can realize function fitting, I have personally debugging, and good results.

Solving Traveling Sales Man Problem by the Algorithm of Simulated Annealing. Src only. 100 free of compile error. Including 2 test cases.-Solving Traveling Sales Man Problem by the Algorithm of Simulated Annealing. Src only. 100 free of compile error. Including 2 test cases.

Mining sequential patterns with constraint (SPADE with constraint)

Fast ICA，一种ICA的快速算法，有完整的论文（两种格式，pdf和ps的）-Fast ICA, the ICA a fast algorithm, a complete paper (both formats, pdf and ps)

一个运用改良的遗传算法来解决旅行商问题的程序-an improved use of genetic algorithms to solve the traveling salesman problem procedures

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.