kalyp是人工生命和神经网络方面架构最全的代码，对于进行仿真代码实现很有参考价值。...

本人编写的QR分解的神经网络算法，该算法用QR分解神经网络的隐含层矩阵，可以作为学术上的比较和分析。适合各种benchmark问题，本人还加了自动产生分类向量和自动适应各种函数的例子-I prepared the QR decomposition of neural network algorithm using QR decomposition neural network hidden layer matrix, can be used as an academic comparison and analysis. Suitable for a variety of benchmark problems, I also added automatically generated classification and automatic vector function to adapt to a variety of examples

用人工神经网络实现格林码的编码，通过对网络的训练实现，输入一串0/1码得到其格林码-using artificial neural networks Green codes, the network through training, input string of 0/1 yards code to be its Green

kmeans算法的java描述，我自己编写的，可以运行的-java kmeans algorithm description, I am prepared, ready-to-run

本书专门介绍遗传算法中的仿生学计算算法的资料，是一本适合于人工智能初学者的好书。-book devoted to the genetic algorithm bionics algorithm information, is a suitable for beginners books on artificial intelligence.

随机神经网络工具箱-random neural network toolbox

flex在线股票系统源代码，是非常实用的一款程序-flex-line stock system source code, is a very useful program

svm training example from svm.org

模拟退火和遗传算法混和求解车辆路径问题的程序，没有解压密码。-simulated annealing and genetic algorithms hybrid vehicle routing problem solving process, not extracting passwords.

本人调试的大作业（源程序模型+论文），主要包括PID控制、模糊控制、神经网络控制、遗传算法优化神经网络控制（使用了遗传工具箱GAOT）对同一系统所作的仿真比较，并加入饱和、死区、时滞等非线性后的响应，具体的分析比较过程论文中写的很详细。-I debug a big operation (source model+ Thesis), including PID control, fuzzy control, neural network control, genetic algorithm optimization of neural network control (using a genetic toolbox GAOT) on the same system simulation by the comparison, and to join saturation, dead zones, such as nonlinear time-delay after the response, the specific analysis of the comparison process paper written in great detail.

程序包含的设置包括：列数、样本总个数、建模样本数、预报因子数、预报对象数、学习因子、 动量因子、训练次数、总体误差、隐节点数。-procedures contained in the set include : number, the total sample number, the number of samples modeling, forecasting several factors, forecasting several targets, learning factor, momentum factor, the number of training, the overall error of hidden nodes.