in vc console operations, Genetic Algorithm for the shortest path, adjacency mat...

简单的人工智能程序ELIZA的C源程序代码 8K-simple artificial intelligence ELIZA C source code 8K

最大概率分词法,这种分词算法能够较好的解决汉语分词中的歧义问题,但分词效率比最大匹配分词算法要低-greatest probability points accidence, Segmentation algorithm can be used to solve the Chinese word segmentation of Ambiguity, but Word efficient than the largest matching segmentation algorithm lower

另一个足球机器人程序，有非常好的参考价值-Another robot football program, there are very good reference value

非线性规划中求解无约束问题中的一个有效方法--模矢法(步长加速法),并用图表示出来步骤.-Nonlinear Programming Solution unconstrained problem of an effective method-- vector mode (step acceleration), and using map out steps.

RBF神经网络应用于IRIS数据集的例子-Am example of RBF-NN applied on IRIS Data Set

Simulated annealing (SA) for the Symmetric Euclidean TSP

convert a matrix to a libsvm format [class1 char1 char2 ... charN class2 char1 char2 ... charN]

基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer t

Particle Swarm Optimization (PSO) for the TSP

matlab小程序，为初学者提供一些常用的例子-There provide some basic matlab programs that will help a biginer a lot.