网上一直没有Harris算法的VC程序 无奈 自己写一个吧 用了两个小时就写好了 非常简陋 大家凑合着看 2005年11月15日...

一个很好用的自适应人工神经网络模型库，包括ADALINE/RBF/多层前向等，另外内含Demo，更易上手-A good use of adaptive artificial neural network model library, including the ADALINE/RBF/Multi-layer and so on, the other containing the Demo, even易上手

吉林大学《计算智能》课程的课件-Jilin University, "Computational Intelligence" Curriculum courseware

MATLAB 下的模式识别特征提取 用于对特征信号的提取-MATLAB under the feature extraction for pattern recognition features of the signal extraction

遗传算法MATLB图像分割程序及例子，希望对大家有用-GA MATLB procedures, there are genetic algorithm selection, crossover and mutation function, some simple examples MABTLAB GA!

这是用实验函数--Ackley函数来检验遗传算法的优越性，其中有点小毛病，就是种群数在20个左右效果最好，一旦增加种群，结果就不大对劲了。忙了好多天，也没调出来，还请各位大侠指点喽-This is the experimental function-- Ackley function to test the advantages of genetic algorithm, with a little fault, is the number of stocks in the 20 best around, once increased stocks, the result is not very comfortable having the. Busier afterwards, it was not stressed out, please Shanhaiguan kind Lackeys

非常好的模式识别教材，适用于研究生教学，或者初学者-Very good teaching pattern recognition, applied to post-graduate teaching, or beginners

本程序用NetLogo开放，模拟行人流。为本人亲字编程。-the procedures used NetLogo open simulated flow. I affinity for the word programming.

一个用于聚类的工具箱，内有主元分析、模糊等技术的Matlab源代码和应用实例程序Demo。-One for clustering toolbox, with principal component analysis, fuzzy techniques, such as Matlab source code and application procedures for Demo.

Principles of Data Mining是数据挖掘领域里面又一本经典的教材，呵呵，希望对大家有帮助-Principles of Data Mining is a data mining inside another in this classic textbook, Oh, you want to help

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