This is a use of MATLAB to use a simple classification of the PCA implementation...

基于C语言人工免疫源程序,包括二进制编码的简单遗传算法源程序，二进制编码的简单克隆选择算法源程序-Artificial Immune-based C language source code, including a simple binary-coded genetic algorithm source code, binary-coded clonal selection algorithm simple source

Genetic Algorithm (GA) based solver for the Traveling Salesman Problem

一个好用的数据挖掘算法，有感兴趣的朋友常联系了。-a user-friendly data mining algorithms, often friends interested in the link.

模糊控制逻辑飞机着陆问题算法的vc编程实现-Fuzzy logic control of the aircraft landing problem vc Programming Algorithm

用于m=2 的机器调度问题，可求出最优解与花费的时间-m = 2 for the machine scheduling problems, the optimal solution can be obtained with the time

用Prolog求解传教士和野人问题 复习经典谓词演算中的归结原理，掌握人工智能程序设计语言Prolog，理解通过搜索求解问题实现人工智能的思想。-Using Prolog to solve the issue of missionaries and Savage Classical Predicate Calculus review summarized in principle, grasp the artificial intelligence programming language Prolog, to understand to solve problems by searching for the realization of the idea of artificial intelligence.

一个hopfield神经网络很好的程序，与大家分享！-Hopfield neural network a very good program to share with you!

多层权核k均值算法，原作者Yuqiang Guan，现在将他共享，与大家共同学习！-Multi-layer k-means algorithm for nuclear power, original author Yuqiang Guan, will now share him with everyone learning together!

自适应线性神经网路应用实例- Auto-adapted linear nerve network application example

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