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five_point
implement of IFSAR using matlab (implement of IFSAR using matlab)
- 2008-04-27 03:47:51下载
- 积分：1
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DCTafftGAOTONG
说明： FFT高通处理图像和DCT高通处理图像，能够通过这两个程序的对比，来了解这两种处理方式的异同之处。(FFT DCT Qualcomm Qualcomm processing images and image processing, be able to compare the two programs, to understand both the similarities and differences between treatments.)
- 2011-02-21 13:14:00下载
- 积分：1
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PD_cascade
PD cascade controller for ball and beam
- 2013-09-04 13:14:26下载
- 积分：1
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pi4dqpsk_Demo
pi/4 modulation techniqe used in mimo
- 2014-09-22 07:04:38下载
- 积分：1
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allan_modified1p22
Alan Variance Calculation
- 2012-02-15 16:41:13下载
- 积分：1
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mbook
经典matlab书籍——《精通MATLABA6.5版》，张志涌等编著，北京航空航天大学出版社。配备书上详细的代码，学好MATLAB必备。
(Matlab classic books- proficient MATLABA6.5 Edition , edited by Zhang Zhiyong, etc., Beijing University of Aeronautics and Astronautics Press. Books with detailed code, learn essential MATLAB.)
- 2008-06-25 08:16:54下载
- 积分：1
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pinpuyi
说明： 这是基于matlab的数字频分析谱仪。有多种滤波器(This is based on the number of frequency analysis matlab spectrometer. There are many filter)
- 2008-11-13 15:31:18下载
- 积分：1
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jsscx1
近似熵MATLAB的计算程序，可以通过此程序对脑电信号EEG进行分析，达到你要的结果。程序仅供参考，里面可能有少许不足之处，希望大家不吝赐教。(approximate entropy MATLAB program, through this procedure right EEG EEG analysis you want to achieve results. Procedures for reference purposes only, which may have some shortcomings, we hope that substantive progress has spared no.)
- 2007-05-11 10:06:13下载
- 积分：1
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Plainness
求峰值点的子程序，可用于信号的频率等参数估计(And the peak point of the subroutine, may be used to signal the frequency parameter estimation)
- 2011-12-12 11:38:40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
