用VC++编写的基本遗传算法的源程序，而且有中文解释，适于初学者。...

神经网络模式识别及其实现，第10章 内含：ART 和VQ 算法。 终于上传完，本来想一次性发上来的，但结果失败了，只好一个一个发。-pattern recognition and neural network to achieve, Chapter 10 consisting of : ART algorithm and VQ. Finally upload End, originally wanted onto a one-time, but the result of the failure, but a one-fat.

神经网络相关算法，使用java语言进行描述的-Neural network-related algorithms, using the java language to describe the

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,

通俗的介绍了RVM与SVM的区别,写的非常通俗易懂-Tutorial of RVM

基本蚁群算法的源代码 经过一些改正 改正了原来的一些错误 可以运行 -The basic ant colony algorithm source code through a number of corrections to correct the original error can run some

用遗传算法解决八皇后问题！ 有比较详细的注释！ 还有有关遗传算法理论的描述。 -Using genetic algorithms to solve the issue of the eight Queen

可视化的手写（鼠标）字母数字神经网络训练程序。-visulized neural network trainning programme。

粒子群算法工具箱，有DEMO程序，可以对16中常见函数测试，并附有神经元网络的训练算法-Particle swarm optimization toolbox, there are DEMO program, you can test for 16 common functions, together with the training algorithm for neural networks

附加了动量项BP神经网络-Additional items of Momentum BP neural network

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.