排序方法，一个简单的排序，原代码和文档。

本程序是具有进化能力的人工生命floy，你会看到越来越具有智能的floy会自发的产生。 -this evolutionary process is the ability of artificial life floy, you will see more intelligent the floy will spontaneously emerge.

这是表达支持向量机一个demo，可以帮助更好的理解svm-This is an expression of support vector machine demo, can help a better understanding of svm

对模式识别中的字符串模板匹配问题进行的研究，设计了对任意字符串进行匹配和字符串相似度，即列文斯顿距离进行计算的算法-right pattern recognition template matching string for the research and design of the arbitrary strings and string matching similarity, Ken Livingstone distance calculation algorithm

采用的是康力山等人确定的实验参数。 对于n个城市的旅行商问题，其参数如下： 初始温度：t0=280, 每一个温度下采用固定的迭代次数L=100n, 温度的衰减系数alpha=0.92 算法停止的准则是当相邻两个温度得到的解变化很小时算法停止。-used the Stanozolol Hill were determined by the experimental parameters. N cities for the traveling salesman problem, the parameters are as follows : initial temperature : t0 = 280, each a fixed temperature of the iteration number L = 100n, temperature attenuation coefficient alpha = 0.92 algorithm to stop the criteria when two adjacent the temperature change was very Solution Algorithm stop.

这是一种利用bp神经网络来解决异或问题的算法.-bp using neural networks to solve the problems or differences algorithm.

经典的BPN人工神经网络例子源码 ，很经典的-BPN classic examples of artificial neural network source code, it is classic

此代码经过大量使用，反映很好，代码优化，效率高-this code after extensive use, reflecting good code optimization, high efficiency! !

Algorithm based on the outline of (Lin, 1965) for unix

基于点轨迹的手势识别.鼠标右键点击开始记录鼠标轨迹,基于记录的轨迹,利用神经网络算法进行识别.-Trajectory based on the point of gesture recognition. By right-clicking the mouse to start recording the track, based on track record, the use of neural network algorithm to identify.

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