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somlvq
This project compares the performance of SOM versus LVQ in classification problems.
Given two data sets:
‘iris.dat’ has 150 patterns of 3 classes with 4 features.
‘wine.dat’ has 178 patterns of 3 classes with 13 features.
For SOM, use its algorithm (not use MATLAB tool), but for LVQ use MATLAB tool.
- 2021-01-03 13:08:57下载
- 积分：1
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atan_cordic
atan function using cordic
- 2010-10-15 19:10:09下载
- 积分：1
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stabrnd
生成alpha 稳定噪声的程序，该程序可以用来生成四个参数的稳定噪声(produce alpha stable noise,it has four parammeters)
- 2011-01-06 20:30:53下载
- 积分：1
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buxhbm
15.11循环码编码和译码,检验并证明其检错纠错能力(it is（15，11)Cyclic code encoding and decoding.)
- 2011-12-25 12:37:17下载
- 积分：1
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diedaifa
这个程序是用MATLAB编的，是用迭代法实现图像的分割，得到感兴趣区域。(This program is retained by MATLAB，and it uses Iterative method to implement image segmentation and obtain the region of interest。)
- 2010-10-11 10:55:06下载
- 积分：1
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bdfc
说明： 解波动方程的matlab 程序，分别用L-W格式，MacCormark格式(matlab program)
- 2011-04-12 21:12:14下载
- 积分：1
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annexamples
《面向MATLAB工具箱的神经网络理论与应用》一书中实例的所有源代码，及详细说明(ANN，matlab,hop,bp,ART)
- 2009-04-23 21:28:30下载
- 积分：1
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AHP
数学建模实验&matlab实现求矩阵特征值特征向量AHP方法的建模与实现 计算矩阵特征值、特征向量；AHP方法的建模与实现
数学建模实验&matlab实现求矩阵特征值特征向量AHP方法的建模与实现(Experimental & matlab implementation of mathematical modeling to evaluate matrix eigenvalue eigenvector method of AHP Modeling and Implementation of calculating matrix eigenvalues, eigenvectors AHP method of modeling and implementation to achieve mathematical modeling experiments & matlab Matrix Eigenvalues and Eigenvectors AHP method of construction Model and Implementation)
- 2010-05-21 09:02:50下载
- 积分：1
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findpeaks.m
finding peak or reversing point from up trend to down trend and you can switch to find trough by findpeak(-x)
- 2011-12-15 12:51:06下载
- 积分：1
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RVM_matlabToolBox
相关向量机(RVM)的matlab源程序，包含快速算法，内含代码使用说明。 RVM采取是与支持向量机相同的函数形式稀疏概率模型，对未知函数进行预测或分类。 优点： (1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布. (2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间. (3) RVM不需要估计过多的参数. (4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好(Relevance Vector Machine (RVM) of the matlab source code, including fast algorithm that contains code for use. RVM support vector machine is taken the same functional form sparse probabilistic model to predict the unknown function or classification. Advantages: (1) is not only the amount of output predicted target point estimates, but also the distribution of predicted values can be output. (2) using a smaller number of support vectors, thereby significantly reducing the output target amount predicted value calculation time. (3) RVM does not require excessive parameter estimation. (4) RVM meets Mercer theorem on the kernel function is not limited, and better adaptability)
- 2013-11-21 11:05:48下载
- 积分：1
