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SIFT
说明： 计算机视觉的一个典型应用。使用SIFT方法做特征提取，然后用于拼接。(A typical computer vision applications. SIFT feature extraction method used to do, and then used for splicing.)
- 2011-04-12 10:25:54下载
- 积分：1
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quad2dg
高斯积分法的matlab编程，目的在于求解一维，二维三维问题的数值积分。(Numerically evaluates 2D integrals using Gauss quadrature.)
- 2013-09-07 11:13:35下载
- 积分：1
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LSSVR1
利用最小二乘支持向量机实现无线传感器网络的目标定位(The use of least squares support vector machine to achieve targeting wireless sensor networks)
- 2014-11-21 13:51:12下载
- 积分：1
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Matlab-optimal
一本用于matlab最优化设计的电子书籍，对初学者有很大的帮助。(A used in matlab optimization design of electronic books, has a great help for beginners.
)
- 2013-11-18 14:47:02下载
- 积分：1
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Based-on-BP-power-load-forecasting
在MATLAB环境下，利用BP神经网络对电力负荷进行预测，编写了代码，并附上了仿真图。(In the MATLAB environment, the use of BP neural network to forecast the power load, writing the code, and attach the simulation diagram
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- 2014-05-13 13:52:31下载
- 积分：1
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dtwgpkub
基于kaiser窗的双谱线插值FFT谐波分析，在matlab R2009b调试通过，与理论分析结果相比，对信号进行频谱分析及滤波，利用贝叶斯原理估计混合logit模型的参数，包括AHP，因子分析，回归分析，聚类分析。( Dual-line interpolation FFT harmonic analysis kaiser windows, In matlab R2009b debugging through, Compared with the results of theoretical analysis, The signal spectral analysis and filtering, Bayesian parameter estimation principle mixed logit model, Including AHP, factor analysis, regression analysis, cluster analysis.)
- 2016-03-30 20:12:32下载
- 积分：1
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MUI_PPM
Comparison of performance for 2-PPM and 2-PAM under two pulse shapes, Gaussian Pulse and Hermit Pulse with Three scenarios are considered (5, 20, and50 users).
- 2013-07-24 20:39:18下载
- 积分：1
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test_lda
lda基本思想是选择使得Fisher准则函数达到极值的向量作为最佳投影方向，从而使得样本在该方向上投影后，达到最大的类间离散度和最小的类内离散度(Linear classification algorithm)
- 2013-08-01 09:36:06下载
- 积分：1
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Fuzzy-neural-network
模糊神经网络逼近二维非线性函数，我自己编译的程序，希望大家喜欢(Fuzzy neural network approach to two-dimensional nonlinear function)
- 2013-03-25 20:55:05下载
- 积分：1
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PCA
PCA算法。PCA的目的是找到能够分离出最大方差的方向，所以首先求原来所有数据三个维度上的协方差，然后求这个协方差的特征值，最大特征值为第一个方向，从此以此类推。(PCA algorithm. The purpose of PCA is to find able to isolate the direction of maximum variance, so first find all the data in three dimensions on the original covariance, and then find the eigenvalues of the covariance, the biggest feature is the first in one direction, from and so on.)
- 2011-05-15 00:25:49下载
- 积分：1
