AIML的实现，AIML是机器人语言中比较适用的原型，希望大家喜欢...

这是神经网络中一个重要的算法，挺有意义的。-This is a neural network algorithm important, quite meaningless.

神经网络中自适应谐振理论模型实现的C语言源程序！-Neural network adaptive resonance theory model of the C language source code!

LM编程算法，在神经网络中有很大应用之处，神经网络源代码,及BP网络训练界面,其中的L-M算法非常实用 -LM programming algorithm, in a large neural network applications, the neural network source code, and the BP network training interface, in which the LM algorithm is very practical

bp神经网络技术用于步态识别的vc源程序

有关智能优化方法(模拟退火,遗传算法,禁忌搜索,等等)-The intelligent optimization methods (simulated annealing,genetic algorithm,tabu search,etc)

用元胞自动机实现单色10000个空间位置的细菌分裂试验的算法C语言代码-Using Cellular Automata 10,000 monochrome realize spatial location of bacteria to split the test algorithm C language code

这是一个HOPFIELD神经网络程序，里面有MAKEFILE，方便读者运行-HOPFILED This is a neural network program, which has MAKEFILE easy operation

非常好的模式识别教材，适用于研究生教学，或者初学者-Very good teaching pattern recognition, applied to post-graduate teaching, or beginners

可以基于遗传算法的网络故障知识获取技术，大学时候老师留的作业自己编的-be based on genetic algorithm network fault access to knowledge, when the teacher left the University of operating their own series

文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层 到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索 路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法 无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum.