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针对SVM法线特征筛选算法仅考虑法线对特征筛选的贡献,而忽略了特征分布对特征筛选的贡献的不足,在对SVM法线算法进行分析的基础上,基于特征在正、负例中出现概率的不同提出了加权SVM法线算法,该算法考虑到了法线和特征的分布.通过试验可以看出,在使用较小的特征空间时,与SVM法线算法和信息增益算法相比,加权SVM法线算法具有更好的特征筛选性能.(Normal feature selection for SVM algorithm only considered normal for the contribution of feature selection, to the neglect of the characteristics of the distribution of feature selection have contributed to the lack of normal SVM algorithm based on the analysis, based on the characteristics of the positive and negative cases emergence of a different probability-weighted normal SVM algorithm, which takes into account the distribution and characteristics of normal. through the test can be seen in the use of smaller feature space, the normal and the SVM algorithm and information gain algorithm, normal weighted SVM algorithm has better performance of feature selection.)

说明：  这个程序是用于读入数据，并对其进行完全设计法滤波(This program is used to read the data , then using filter to deal with)

运用粒子群和改进粒子群算法求解典型函数 具有可视化的功能(And improve the use of particle swarm particle swarm algorithm with a typical function of visual function)

book for matlab for beginners

A pv module is modeled in MATLAB/SIMULINK environment with use of mathematical equations that describe pv module s schematic model. This model has 2 inputs named Vpv and insolation and has 2 outputs named Ipv and Ppv. Insolation signal is varying.

VC++调用matlab画图，效果很好！值得看(VC++ Call matlab drawing, the effect is very good!)

车牌识别， 源程序，车牌识别matlab 程序(License plate recognition source)

GMM PARAMETERS AN BE EASILY IMPLIMENTEED USING EM TECHNIQUE.EVENTHOUGH THIS MODEL IS COMPLEX ,E XPECTATION MAXIMAISATION FINDS AN EASYWAY TO CALCULATE THE PARAMETERS IN AN EFFICIENT WAY.

本文是music算法的一种改进，其具有更好的性能，以及能够满足大多数用户的需求，希望会给大家带来方便(This article is a modified music algorithm, which has better performance, and the ability to meet the needs of most users, I hope we will bring convenience)

冈萨雷斯数字图像处理matlab源代码含有大量的源代码程序，方便学生的学习和查找。(Gonzalez digital image processing matlab source code contains a lot of source code program to facilitate student learning and search.)