基本遗传算法的源代码，比较各代种群的进化。

《神经网络模式识别及其实现》源程序 几个VC编写的神经网络示例-neuronetwork

BP算法实现异或问题,采用S型函数的前向多层神经网络及其逆推学习算法-BP algorithm differences or problems, use of the S-Function to the multilayer neural networks and learning algorithms Backstepping

用bp实现的数字识别程序，用到了经典的bp算法，可移植到其他应用程序中去-bp achieve with the digital identification procedures, the use of classical bp algorithm can be transplanted to other applications go

神经网络隐节点合成算法，用于神经网络泛化能力的提高，和大家共享！-Hidden node neural network synthesis algorithm for neural network generalization capability, and the U.S. share!

自适应共振理论（ART）-Adaptive Resonance Theory (ART)

也是学习别人的程序，不过他没有把核心代码给我-also learn from others procedures, but he did not take me to the core code

地球模拟器，最经典的人工生命模型,研究人工智能的必看。一般地说，生命都具有新陈代谢、复制和进化的能力。在自然界中，生物是由有限的食物供给和有限的生存空间约束的。在Tierra中，“生物”由一系列能够自我复制的机器代码或程序组成，它在计算机中的复制分别受到计算机的存储空间和CPU时间约束。能有效地占有内存空间和利用CPU时间的生物体，将具有更高的适应度，传递到下一代的机会就越大。 在Tierra中，计算机的RAM（随机访问存储器）中有一块专门的空间，这个空间中放置了一个“祖先有机体”，该祖先有机体根据它的汇编程序代码中的指令开始复制对它的生存是基本的代码。随着有机体的数目的增加，RAM中的空间减少了，因此有机体为了自己的生存空间开始竞争。-Earth Simulator, the most classic of artificial life model, the study of artificial intelligence Watchable. Generally speaking, life is metabolism, reproduction and the ability of evolution. In nature, biomass is limited by the supply of food and limited living space constraints. In Tierra, "biological" to a series of self-replicating machine code or procedure, in which computers were copied by the computer storage space and CPU time constraints. Effective occupy memory space and CPU time using the organisms, will have a higher degree of adaptability, conveying to the next generation the greater the chances. In Tierra, computer RAM (random access memory) is

收集的关于遗传算法的模糊搜索的ppt资料，有兴趣的可以研究一下-Collected on the genetic algorithm ppt fuzzy search information, interested can look at

自组织系统Kohonen网络模型。对于Kohonen神经网络，竞争是这样进行的：对于“赢”的那个神经元c，在其周围Nc的区域内神经元在不同程度上得到兴奋，而在Nc以外的神经元都被抑制。网络的学习过程就是网络的连接权根据训练样本进行自适应、自组织的过程，经过一定次数的训练以后，网络能够把拓扑意义下相似的输入样本映射到相近的输出节点上。网络能够实现从输入到输出的非线性降维映射结构：它是受视网膜皮层的生物功能的启发而提出的。~..~-Kohonen network model. For Kohonen neural network, competition is this : For the "winner" of neurons c, in its switching around the region neurons in varying degrees, to be excited, and the switching outside the neurons were inhibited. Network learning is a process in the network connecting the right under the training samples for adaptive, self-organizing process, after a certain number of training, network topology can sense similar to the mapping of the input samples similar to the output nodes. Network can be achieved from input to output of nonlinear reduced-dimensional mapping structure : it is subject to retinal cortex of the biological function inspired by. ~ ~ ..

基于SVM，分本分类，，是关于两类分类的比较好的工具，国外网站提供，效果很好的-based SVM, the sub-classification is on the classification of the two types of relatively good tool, overseas sites provide good results, the