Visual C++实现的基因遗传算法库源代码以演示程序Free Source Code for Genetic algorithm 2008年05月21日...

经典的粒子群源代码！详细的备注、严谨的语句是初学粒子群的好东西-The classical particle swarm source code! Detailed comments, rigorous statement is a good thing novice PSO

一个简单的用MATLAB写的BP算法源程序。-a simple written in MATLAB BP algorithm source.

L-M算法。除了动量法(基于梯度下降的训练算法)外，学习率自适应调整策略是BP算法改进的另一种途径，它利用Levenberg-Marquardt优化方法，从而使得学习时间更短。其缺点是，对于复杂的问题，该方法需要很大的存储空间。 -L-M algorithm. In addition to momentum (based on the gradient descent algorithm for training), learning rate adaptive strategy is to improve the algorithm BP Another approach, which uses Levenberg-Marquardt optimization method, which makes learning time is even shorter. Its weakness is that the complex problems that the method requires a lot of storage space.

支持向量机多参数自动选择优化程序！机器学习，数据挖掘工具！matlab版！-Support vector machine multi-parameter optimization procedure automatically selects! Machine learning, data mining tool! matlab version!

ai 代码的C++实现，在VC6.0下运行通过-ai code of C++ to achieve，vc++ Running through

数据挖掘算法BIRCH源码 c语言版本。- Data mining algorithm BIRCH source code c language edition.

模拟蜜蜂采蜜的具有人工生命特性的VB程序 可以作为学习人工生命的一个不错的入门-simulation honey bees with artificial life characteristics of VB program can be used as learning artificial life a good entry

The Netlab toolbox is designed to provide the central tools necessary for the simulation of theoretically well founded neural network algorithms and related models for use in teaching, research and applications development. It contains many techniques which are not yet available in standard neural network simulation packages-The toolbox is designed to provide t he central tools necessary for the simulation o f theoretically well founded neural network al gorithms and related models for use in teaching , research and applications development. It c ontains many techniques which are not yet avail able in standard neural network simulation pac kages

人工智能BP问题的C语言实现，BP算法可见人工智能教材-the C language, BP artificial intelligence algorithms can be seen teaching

用遗传算法解决01背包问题，内附测试数据。-Using genetic algorithms to solve knapsack problem 01, enclosing the test data.