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SVMNR
支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。
(Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network )
- 2021-03-03 21:59:32下载
- 积分：1
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Diff_Loss
电波传播中的绕射分析，需要给出地面剖面貌一新模型(the diffraction loss of the radio propergation)
- 2011-09-06 16:33:26下载
- 积分：1
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network
神经网络的matlab程序，可以作为入门学习，欢迎使用(networ in matlab)
- 2009-12-02 02:51:43下载
- 积分：1
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RSS-Cramer_Rao
RSS测距定位模型的Cramer_Rao界分析(RSS Ranging Positioning Model Cramer_Rao sector analysis)
- 2010-01-04 17:17:38下载
- 积分：1
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VC-and-MATLAB
VC6_0与MATLAB7_x混合编程方法研究(VC6_0 and MATLAB7_x mixed programming method)
- 2013-11-16 11:01:44下载
- 积分：1
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DCT
读入一幅256*256图像，将它分成8*8pixel的若干块，对每块做DCT变换，保留Z型顺序的若干变换系数，进行IDCT重构图像，计算重构图像的信噪比(Reading a 256* 256 image, it will be divided into several blocks of 8*8 pixel, keep each optimal Z transformation of sequence number, IDCT transform coefficient calculation reconstruction image reconstruction images,and the signal-to-noise ratio)
- 2010-07-02 22:08:40下载
- 积分：1
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tutorial2
Tutorial matlab untuk pemula
- 2012-04-16 22:12:54下载
- 积分：1
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2
说明： 动态聚类的k均值算法－－用k均值算法解决动态聚类问题(Dynamic clustering k-means algorithm- using k-means algorithm to solve the issue of dynamic clustering)
- 2008-07-22 12:06:58下载
- 积分：1
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fem
用三角形线性元方法求解以下二维边值问题：- Δu = f,u|partialΩ = g.(FEM sssssssssssssssssssssssss)
- 2014-05-10 00:03:10下载
- 积分：1
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dea matlab
说明： 求解DEA CCR BCC 超效率 matlab程序(Solve the DEACCR BCC super efficiency matlab program)
- 2019-02-20 10:25:28下载
- 积分：1
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