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fft-experiment
（实验）快速傅里叶变换及其应用。包含fft实验的所有源程序，实现fft的基本功能。((Experimental) Fast Fourier Transform and Its Applications. Contains all the source code of the fft experiment, to achieve the basic functions of the fft.)
- 2012-07-15 19:14:30下载
- 积分：1
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fuzzpid
这个是模糊控制pid的模块。应该能帮到大家吧。。。加油(This is fuzzy control pid modules. Learning purposes)
- 2012-08-30 01:10:09下载
- 积分：1
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Introduction to Hybrid Beamforming
说明： 该示例介绍了混合波束成形的基本概念，并说明了如何模拟这种系统。(This example introduces the basic concept of hybrid beamforming and shows how to simulate this system.)
- 2020-11-02 10:13:11下载
- 积分：1
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FEM
computed distribution of the magnetic field Hz
within an wvolving 2D dielectric mircopille with initial r=667nm
run in COMSOL with Matlab
- 2009-05-14 22:03:23下载
- 积分：1
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EDand
基于能量检测的合作检测算法，判决方式为传统硬判决中的and判决(coorporation detection)
- 2012-11-20 21:16:06下载
- 积分：1
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powerpu
功率谱图子程序源码.经过实验,和大量阅读资料最好得到最好的一种算法.(power spectra subroutine source. Through experiments, and a large number of reading material should preferably be the best algorithm.)
- 2007-01-02 00:09:11下载
- 积分：1
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frftlvbo
关于分数阶傅里叶变换的源程序及一些应用，对初学者有很大帮助。(Some applications on the source and Fractional Fourier transform of great help for beginners.)
- 2015-04-01 17:59:05下载
- 积分：1
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AFSA-CS-MP
严格按书上传统原子库的MP稀疏分解~~~~~~~~~~~~(In strict accordance with the traditional book MP sparse decomposition atomic library)
- 2013-12-28 10:33:15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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fitting_plane
用matlab实现的平面拟合算法，只要输入不少于三个的空间点坐标(plane_fitting)
- 2010-06-23 20:53:39下载
- 积分：1
