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condmutualinfo
计算时间序列的熵信号，用以获取信号统计分析(Calculate the entropy of the signal time series, statistical analysis to obtain the signal)
- 2010-09-25 21:51:36下载
- 积分：1
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GA
说明： 有关于各式matlab的ga码，可以参考看看(Matlab-ga about the various types of code, can refer to see)
- 2011-01-26 21:06:04下载
- 积分：1
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Canny
Canny Edge Detection
- 2010-11-12 15:44:20下载
- 积分：1
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codes
基于累计量的奇异值－总体最小二乘法求AR参数 用奇异值－总体最小二乘法求AR参数 一般最小二乘法求AR参数 根据AR参数和自相关函数以及AR阶数用Cadzow谱估计子求出频谱密度(Based on the cumulative amount of singular value- total least squares method for AR parameter using singular value- total least squares method for AR parameter ordinary least squares method for AR parameter in accordance with AR parameters and the autocorrelation function, as well as the order of AR spectral estimation using Cadzow sub-spectrum density obtained)
- 2009-05-13 17:07:41下载
- 积分：1
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DWT_test2
matlab图形图像处理源程序 实现DWT水印的嵌入和提取 (graphic image processing matlab source code to achieve DWT watermark embedding and extraction)
- 2010-12-15 10:59:01下载
- 积分：1
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ldpc
Low density parity code
- 2012-03-30 06:44:31下载
- 积分：1
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signal
信号相加,卷积的应用，从别的地方学习的，貌似有点问题，大家可以参考一下(From other place)
- 2013-03-20 09:26:17下载
- 积分：1
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BrillouinZoneSamplingOfAPeriodicChainWithNSites-a
mathematica 包，非周期链的布里渊区选样
- 2015-01-17 20:54:59下载
- 积分：1
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1807.01622
深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)
- 2020-06-23 22:20:02下载
- 积分：1
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abc
异步电机起动仿真程序（abc坐标系下）两个文件要放在一个文件夹下运行(asynchronous start program)
- 2010-08-06 00:49:45下载
- 积分：1
