Framework for Genetic Algorithm

一个可自我学习的人工智能小模型，可进行训练，也可进行提问-a self-learning artificial intelligence small, and can be used for training, may also question

模拟退火算法用于求解旅行商问题的matlab源程序-simulated annealing algorithm for solving the traveling salesman problem Matlab source

利用最大匹配法进行汉语句子的分词 最大匹配算法是最常用的分词算法，简单实用正确率可达到80%以上-the maximum matching method for the Chinese Sentence Word maximum matching algorithm is the most commonly used word segmentation algorithm, simple and practical accuracy rate can reach more than 80%

模式识别_贝叶斯分类器-pattern recognition _ Bayesian classifier

本程序利用蚂蚁算法解决TSP（旅行商问题）问题-This procedure using ant algorithm to solve TSP (Traveling Salesman Problem) problem

程序用C语言实现了贝叶斯在数据挖掘中分类和预测中的应用，通过程序可以很好的进行分类和预测。-This program use C language to realize the application of bayes in data mining and forecasting,you can use this program to make a good classification and forecasting

无约束条件的模拟退火算法 -Unconstrained conditions of simulated annealing algorithm

exmat - The Expression Template Matrix Library,是矩阵运算模板库-exmat- The Matrix Expression Template Library, yes matrix calculation Template Library

这是一个我自己编写的JAVA程序，可以运行的。思想是将KMEANS和神经网络结合起来实现模式的分类。-This is a Java program writen by meself which can be run.The chief concept is to combine KMEANS and NN to achieve classification of pattens.

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