无解压密码可放心使用

基于神经网络的手写体识别程序，可能对感兴趣的朋友有所帮助-Based on neural network handwriting recognition program may be helpful to interested friends

模式识别工具包，有很多模式识别的算法，可以在matlab中调用-Pattern recognition tool kit, there are many pattern recognition algorithms, you can call matlab

关于神经网络方面的130个程序，采用matlab语言，比较全-On the neural network 130 procedures, the use of matlab language, compare the whole

模拟退火算法的源代码 可以执行 其中提供了指导说明-Simulated annealing algorithm can be implemented in the source code which provides guidance notes

该程序通过遗传算法对图像进行聚类分析，并实现了图像的分类功能，分类效果明显较其他算法好-The genetic algorithm procedure for cluster analysis of the images and to achieve the classification of image features, the classification results significantly better than the other algorithms

层次聚类算法中的cure算法，它利用代表点来达到聚类目的-hierarchical clustering algorithm cure the algorithm, which uses representatives point to achieve clustering purpose

这是一个模式识别中的parzen窗的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition in a simple window parzen Category simulation examples, one of female.txt and male.txt training samples, test.txt is the measurement, classification effect is very good for beginners will be learning model has very big help.

Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42. 使用神经网络集成方法诊断糖尿病,肝炎,乳腺癌症的案例研究. -Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.

he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.

基于k均值聚类学习算法的rbf神经网络实现-K-means clustering-based learning algorithm of rbf neural network