模糊神经网络的源码,课程设主计.主要用来识别电子鼻的信号....

神经网络vc源代码神经网络的VC++代码呀-Neural network source code vc neural network VC++ Code you

BugBots是一个游戏，描述它的最好方式是它是一个测试人工智能的框架。 来源: http://sourceforge.net/projects/bugbots/-BugBots is a game, describing it as the best method is that it is a test of artificial intelligence framework. Source : http://sourceforge.net/projects/bugbots/

我做的一个用matlab程序编写的BP算法，是是我毕设的一部分，程序运行绝对没有问题，欢迎大家指正。-I do a Matlab programming with the back-propagation algorithm, is part of a complete, running absolutely no problem, we are happy to correct.

求解tsp问题的模拟退火源码，数据文件请仿照文件中的input.txt自行创建-the simulated annealing source, data files copied documents requested the creation of their own input.txt

K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.

这个程序是个神经网络提供学习能力的实例，是前一个simpleann的功能加强版。-this procedure is a neural network learning ability example is the former simpleann an enhanced version of the function.

一个用VC实现的高效bp神经网络算法源代码，希望对大家有帮助.-VC realize an efficient use of bp neural network algorithm source code, and they hope to help everyone.

用遗传算法解决01背包问题，内附测试数据。-Using genetic algorithms to solve knapsack problem 01, enclosing the test data.

Autolife模型是一个能够进行“开放式进化”的人工生命系统。每个Agent模型采用可以变化规则表长度的有限自动机模型建模。一方面Agent可以进行自我繁殖，同时模型中的选择机制没有采用显式的适应度函数而是采用能量消耗的简单模型而自发涌现出来，所以可以认为Agent模型是一个类Tierra系统。然而与Tierra、Avida等数字生命模型不同的是，Autolife模型进行了大大的简化，它界面友好，操作直接。虽然没有给每个Agent装配一个虚拟计算机，但是Agent与环境的耦合则可以看成一个图灵机模型，因此Agent可以通过变异而“任意”的编程序。通过Autolife模型，首先人们可以看到一般的生态系统中共存的现象：生物的大爆炸、大灭绝，Agent进化得越来越聪明；其次，用户可以通过变化不同的食物添加规则探索Agent与环境的关系；最后，如果允许Agent通过播种改变环境自动产生食物，那么组织的涌现就是一种不可避免的结果。Agent构成的组织具有自主运动的特性，还可以进行自我修复，可以说Autolife中的组织是一些真正的“活体”。-Autolife model is to conduct an "open-evolution" artificial life system. Each Agent can be used model changes the rules limited the length of the table automatic machine modeling. One Agent can be self-propagation, model selection mechanism not used Explicit the fitness function instead of using the energy consumption of a simple model to emerge spontaneously, so that the agent can be a model category Tierra system. But with Tierra, Avida digital life model is different Autolife model for greatly simplified its

用C++ Builder的分形程序，包括一个分形生物。 -With C++ Builder fractal procedures, including a fractal biological.