用C++实现遗传算法求解TSP问题.txt

基于matlab的小波分析工具箱WAVELAB850-based on the wavelet analysis Matlab toolbox WAVELAB850

神经网络设计书籍配套源码（matlab平台）-neural network design books matching source (Matlab platforms)

本算法采用最基本的遗传算法编写，有利于初学者掌握遗传算法的构成。-This algorithm is written by the most fundamental genetic algorithm,benefit the beginner to know the structure of genetic algorithm

遗传算法,网络故障的源代码!值得学习!欢迎下载!-genetic algorithms, network fault of the source code! Worth learning! Welcome to download!

一种通过自组织竞争学习网络实现数据降维和可视化的单层神经网络模型。用此算法可以把输入空间的多维映射到低维的（一维或者二维）的离散网络上，并将保持相同性质的输入数据在映射到低维空间时的拓扑一致性。iris以及letter两个数据集进行分类-A competitive learning through self-organizing network for data dimensionality reduction and visualization of single-layer neural network model. Using this algorithm can be multi-dimensional input space is mapped to the low-dimensional (one-dimensional or two-dimensional) discrete network, and will remain the same as the nature of the input data is mapped to the low-dimensional space of topological consistency. iris as well as the letter the two data sets to classify

HOPFIELD的一个经典用例，解决TSP问题，代码完整，是我自己编写的哦-HOPFILED cases with a classic, TSP solution, code integrity, I was prepared oh

产生应用区系统中的噪声可以改变其分布的-Application areas have a system noise can change its distribution

一个DoS下的神经网络程序.-the neural network programming.

包分类算法Hypercuts算法，包分类算法Hypercuts算法-Hypercuts

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,