In this paper, we designed a 3

BLDC建模及神经网络控制，建模讲述的很详细，难得的论文。-BLDC modeling and neural network control, modeling, described in great detail, rare papers.

识别数据集,可以用来作为,分类的实验数据.类别:模式识别-Identification of data sets, can be used as, the classification of experimental data. Category: Pattern Recognition

关联规则中的频繁项集生成算法genmax，可在linux和windows下编译运行，可能不太容易看懂。-association rules of frequent item sets genmax generation algorithm, and the Linux compiler running under windows, it may not be easy to understand.

单层线性神经网络实例-single example of linear neural network

决策树分类中经典算法的ID3和Ｃ４．５代码公共包！-Classical decision tree classification algorithm ID3 and C4.5 code public packet!

图像模式识别的聚类器设计，采用VC++实现，和分类器设计相对应。-the designation of image pattern recognation,VC++ is adopted, along with the sort ware designation

蚁群算法及其在TSP中的应用。一篇很好的论文。-Ant colony algorithm and its application in the TSP. A very good paper.

可以用C++语言开发各种神经网络：BP，RBF，HOP~…………，使用前请看看说明文档，然后建立一个自己的项目文件，只要能明白作者的思路就能很方便地进行各种神经网络的设计，本人现在就在一个系统中使用它，目前这个开发包的版本已进入0.7了，但功能差不多，本人认为0.5这个版本用着也很方便，所以把他发来，大家共享。-C language can be used to develop a variety of neural networks : BP, RBF, HOP ~ ... ... please look at the use of pre-documented, and then the establishment of a project document, you will be able to understand the author"s ideas will be very convenient for the various neural network design, I in a system in which to use it, the current version of the development kit has entered 0.7, but the function almost, I think that this version of 0.5 is also very convenient, so he made to share.

基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer t

改进遗传算法程序-improved genetic algorithm program