GUI-examples

最小二乘支持向量机的matlab工具包，包括源程序和使用说明(Least squares support vector machines matlab toolkit, including the source and use)

How to use the ordinary Voronoi partition generator. The m-files also have their own help documentation that you can view in Matlab. This code creates a bounding polygon that is a square. Then it randomly creates 10 points and then creates and draws the partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(2, 10) regions = voronoi(bounds, points) drawRegions(bounds, regions) ================================ This is a brief set of instructions on how to use the Multiplicatively- weighted Voronoi partition generator. Each of the m-files has a help section, so in Matlab you can call: help mwvoronoi help drawRegions Here is an example that creates a bounding polygon, a square with side length 10. Then it randomly creates six points with random weights. Finally, the code draws the resulting partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(3, 6) regions = mwvoronoi(bounds, points) drawRegions(bounds, regions) Enjoy!

Three phase full wave rectifier simulation using matlab, simulink

fit in processing with function

可以进行信道估计,准确得到信道的冲激响应，比普通的LS算法性能较好。(evaluate the pulse response of channal)

data hiding................................................

遗传算法和神经网络很重要的程序 有关这个的内容 希望你不要错过(Genetic algorithms and neural networks are very important program hope you do not miss on this content)

Hotkey traping all Keys grom applications

说明：  切比雪夫滤波器，切比雪夫I型滤波器设计，双通切比雪夫滤波(Chebyshev filter,Design of Chebyshev I filter, double pass Chebyshev filter)

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.)