] [Machine learning text classification source code] [machine learning text clas...

基于信息熵的免疫算法是一种修正的免疫算法，方法并不复杂，对于特定问题效果较好-Entropy-based immune algorithm is a modified immune algorithm, methods are not complicated, for the specific problem better

Biome是一个c++库，它的目的是个人_基于代理的模拟器（象 Swarm 或EcoSim）。它试图尽可能达到高效并且同时有好的设计-PROJECT is a c library, it is aimed at individual _ Agent-based simulator (like Swarm or EcoSim). It seeks to achieve efficient as possible while it is good design

张文修老师主编的详细介绍遗传算法及其数学基础的一本书，研究遗传算法的一本很好的参考书，特别推荐！-Edited by Prof. Zhang Wenxiu, detailed genetic algorithm and its mathematical foundations, a very good reference book to study the genetic algorithm, strongly recommended!

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.

基于ICA的信号分离程序，是一个很好的信号处理方面的程序-based ICA signal separation procedure is a very good signal processing procedures

matlab实现机器人基于两点法的路径规划，路径规划的实用例程-Matlab 2:00 achieve robot path planning method, path planning practical routines

这个程序对快速实现模糊逻辑控制很有用。假设这个逻辑控制有两个输入和一个输出，用各种选择来实现它。-This procedure is the rapid realization of the usefulness of fuzzy logic control. Assuming that there are two logic control inputs and one output, with various options to achieve it.

一个模糊的YBP训练程序，初学者应该从入门开始学习

遗传算法工具箱及应用，包括了美国那个大学遗传工具箱的应用说明，很好用-Genetic algorithm toolbox and its application, including the United States that the University of the genetic toolbox of application notes, very good use

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.