神经网络 MCP运算法

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

神经网络算法-Neural network algorithm

C#版遗传算法解TSP问题-C# version Genetic Algorithm for the Traveling Salesman Problem

fortran 语 言 编 写 基 本 遗 传 算 法 源 程 序 fortran Genetic Algorithms written by FORTRAN

加了注释的c代码（complexNetwork）源代码，下载后就可以直接使用-Added a comment c code (complexNetwork) source code, downloads can be used directly after

BP神经网络算法，可以调节隐层，输入输出等-BP neural network algorithm, can be adjusted hidden layer, input and output, etc.

对于具有一定规模的TSP问题的求解，至今没有较完善的求解办法。本程序是用遗传算法来求解TSP问题。-for a certain size of the TSP solution and there has been no better way to solve. This procedure is used genetic algorithms to TSP.

本人编写的GramSchmidt神经网络算法，和传统的神经网络算法比较，该算法能自动产生神经元，节约时间，速度快！ 本人还加了自动产生分类的方法和适合各种函数的转换，该zip包括两个文件，一个是GramSchmidt正交分解，是个子函数，主函数是orth_GramSchmidt，放在同一目录下，就可运行，可以对benchmark的逼近和分类问题提供解决方案-I prepared GramSchmidt neural network algorithm, and the traditional neural network algorithm, the algorithm can automatically generate neurons, saving time, fast! I also added a method of automatically generated classification and conversion for a variety of functions, the zip includes two documents, one is GramSchmidt orthogonal decomposition is a sub-function, the main function is orth_GramSchmidt, on the same directory, you can run can benchmark approximation and classification problems to provide solutions

GaPlayground:一个非常好的遗传算法demo.-GaPlayground : a Genetic Algorithm very good demo.

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,