经典的BNP神经网络例子

通过C4.5 的实现可以进行构建决策树 来进行有效的分类-Through the realization of C4.5 decision tree can be constructed to carry out an effective classification

基于小波神经网络算法的流域日径流预报模型，该例程为结合型小波神经网络的例子-based on wavelet neural network algorithm watershed day runoff forecasting model, the combination of routines for wavelet neural network examples

用于求解TSP问题的c++程序，已经试运行过-BasicAntColon AlgorithmC++forTSP

在对神经网络了解一点点后就可看懂这个，介绍MATLAB神经网络工具箱运用

一个包含丰富内容的流形学习算法工具包，有图形示例文件demo.fig，包括laplacian特征映射算法、流形规则调整、svm分类算法等内容，希望对研究机器学习的朋友有用。-contains a rich content of the manifold learning algorithm tool kit, graphics documents demo.fig example, laplacian features include mapping algorithm manifold rules adjustments svm classification algorithm, want to study machine learning useful friends.

这是一个关于遗传算法的程序，包括了算法的各个过程，其实我对遗传算法也不很懂，也是刚刚开始学习阶段，希望一起进步-This is a genetic algorithm on the procedures, including all the process of the algorithm, in fact, I do not很懂of genetic algorithm, is just the beginning stages of learning, hope and progress together

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,

BP神经网络用于函数拟合与模式识别的Matlab示例程序,说明很详细,并且代码可以直接拿来用-BP neural network for function fitting and pattern recognition procedures Matlab examples to illustrate in great detail, and the code can be directly used by

c语言神经网络程序包-neural network package

很不错的xml解析工具，写的非常好，值得用一下-A very good tool for xml parsing, writing very well, it is worth to use what