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stanford-deep-learning-matlab-code

于 2018-06-07 发布

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下载积分: 1 下载次数: 2

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代码说明：

说明： Stanford 大学的深度学习源代码，可用于模式识别和预测，比较稳定。(Stanford University's deep learning source code can be used for pattern recognition and prediction, and is relatively stable.)

文件列表：

cnnConvolve.m, 3702 , 2013-04-12
cnnExercise.m, 9289 , 2013-04-12
sparseAutoencoderLinearCost.m, 4261 , 2013-04-11
softmaxExercise.m, 4796 , 2013-04-10
checkStackedAECost.m, 1745 , 2013-04-10
softmaxCost.m, 1281 , 2013-04-10
softmaxPredict.m, 849 , 2013-04-10
sparseAutoencoderCost.m, 3865 , 2013-04-09
stlExercise.m, 5514 , 2013-04-07
feedForwardAutoencoder.m, 1304 , 2013-04-07
loadMNISTImages.m, 835 , 2013-04-03
pca_gen.m, 6377 , 2013-04-03
pca_2d.m, 3426 , 2013-04-01
trainMNIST.m, 1830 , 2013-03-11
train.m, 5796 , 2013-03-11
sparseAutoencoderCost - 副本.m, 4104 , 2013-03-11
computeNumericalGradient.m, 1115 , 2013-03-11
sampleIMAGES.m, 2120 , 2013-03-10
linearDecoderExercise.m, 5346 , 2011-07-16
displayColorNetwork.m, 1233 , 2011-05-22
stackedAEExercise.m, 7412 , 2011-05-13
softmaxTrain.m, 1891 , 2011-05-10
stackedAEPredict.m, 1296 , 2011-05-09
stackedAECost.m, 3100 , 2011-05-09
params2stack.m, 1174 , 2011-05-09
stack2params.m, 1653 , 2011-05-09
display_network.m, 2647 , 2011-04-29
sampleIMAGESRAW.m, 696 , 2011-04-29
loadMNISTLabels.m, 516 , 2011-04-25
checkNumericalGradient.m, 1982 , 2011-01-05
initializeParameters.m, 622 , 2011-01-05
cnnPool.m, 1641 , 2013-04-12

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decisor_correlacao_generalizado
It s a neural network decisor for Pareto set based in cross-correlation between a specific noise model and sucessive aproximation errors of the neural network that generates each solution of Pareto set.

2010-11-17 06:02:13下载

积分：1
backwardflow
一种前推回代的配电网潮流计算，是配电网潮流计算的基础(A former back-generation distribution power flow calculation, distribution power flow calculation is the basis of)

2008-05-17 14:44:25下载

积分：1
fuzzy-frost-filter
fuzzy applied local statistic filter

2013-02-19 21:59:58下载

积分：1
source
输入信号为方波信号+高斯白噪声，方波信号基频为1kHz，幅值为1，高斯白噪声方差为0.2，均值为0，采样频率为20kHz。试用最大熵估计法估计此信号的AR模型及其功率谱，并分析结果(The input signal is a square wave signal+ Gaussian white noise, square wave signal fundamental frequency 1kHz, amplitude is a Gaussian white noise variance is 0.2, the mean is 0, the sampling frequency is 20kHz. Try maximum entropy estimation method to estimate the AR model of this signal and its power spectrum, and analyze the results)

2013-10-29 17:50:45下载

积分：1
PCA-BEL(ToolBox1.0)
又提供一基于生理的情感神经网络，包含杏仁体，丘脑，视觉神经等重要元素，可用于分类与预测，性能优越于SVM，BP(The use of neural network based on emotional physiology, contains important elements of the amygdala, thalamus, optic nerve, etc., can be used for classification and prediction, superior performance to SVM)

2015-03-17 18:23:36下载

积分：1
A-duffing
用于检测微弱信号的Duffing系统，这是我的毕业设计一部分，因为我做的时候也曾求取过很多资料，但是并没有找到，因此在我完成之后，上传这个代码，希望这个能帮助别人。里面还有后续做的GUI界面实现的Duffing振子检测功能，可检测信号幅值和频率。(The Duffing system for detecting weak signals, this is part of my graduation project, because I also asked for a lot of information when I did it, but it was not found, so after I finished, upload this code, I hope this can help others.)

2019-06-08 20:47:42下载

积分：1
juanji
求连续函数的卷积，利用matlab中的conv函数实现(Seek a continuous function of the convolution, using the conv function in Matlab to achieve)

2012-06-09 09:54:40下载

积分：1
simInit1
matlab code for polyphase implementation

2013-03-19 09:48:40下载

积分：1
Hough
用HOUGH检测直线的时候是两个未知量,因为轴半径和相角可以确定一条直线，在HOUGH域出现累计最大点就可能是原域中的直线。现在回到圆的检测上来，确定一个圆需要什么：X坐标，Y坐标，半径三个未知量吧，好了，现在，你做一个三维空间的HOUGH域，以这三个未知量作为三个轴，现在按照一定步长进行三重循环，在最内层循环是这样的，X,Y 确定，以不同的半径进行搜索，如果你的X,Y刚好就是实际图像的X,Y处，半径又搜索到实际真实的半径(The HOUGH detect straight when two unknown amount of axis radius and phase angle can be determined in a straight line, in the HOUGH domain cumulative maximum point may be the straight line of the original domain. Now back to the circle detection up to determine what a circle: X coordinate, Y coordinate, radius three unknowns, now, you do the HOUGH domain of a three-dimensional space, three to three unknown quantity as axis, in accordance with a certain step size triple loop, is such that in the innermost loop, X, and Y determined search for a different radius, If you X, Y, exactly is the actual image of the X, Y, at the radius radius of the search to the actual real)

2013-03-03 13:05:47下载

积分：1
OSOS
vision task for me you who can be will feel

2011-12-21 01:41:11下载

积分：1

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