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matlab
matlab应用于捷联惯导系统初始对准滤波技术程序(matlab applies SINS initial alignment procedures Filtering Technique)
- 2009-03-23 15:44:25下载
- 积分：1
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7-bus-power-flow-calculation-prog
7节点潮流运算的MATLAB程序,包括导纳、雅克比矩阵生成，运算结果输出等部分(7-node system flow calculation procedures, including the admittance matrix, such as the generation of Jacobian matrix, and finally output the results of current operations)
- 2011-04-20 09:09:05下载
- 积分：1
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hough_circle
matlab中实现hough圆检测的源代码，检测效果还可以，测试可用(find circles in an image using matlab)
- 2010-05-15 11:04:30下载
- 积分：1
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jeifai
小波包分析提取振动信号中的特征频率，仿真效率很高的，采用累计贡献率的方法。( Wavelet packet analysis to extract vibration signal characteristic frequency, High simulation efficiency, The method of cumulative contribution rat.)
- 2016-08-31 13:41:39下载
- 积分：1
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V2G
说明： V2G系统simulink仿真图以及电动汽车充电和放电图，内容齐全(V2G system simulink simulation diagram and electric vehicle charging and discharging diagram, complete content)
- 2021-04-20 22:58:50下载
- 积分：1
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AFT
Simon haykin《自适应滤波器原理》的书中例子的matlab代码(Simon haykin " Adaptive Filter Theory," the book example matlab code)
- 2009-11-07 13:30:32下载
- 积分：1
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DCS_spectrum_sensing
说明： 分布式压缩感知，DCS_SOMP算法。用于稀疏信号的分布式恢复。(Distributed compressed sensing, DCS_SOMP algorithm. Distributed for sparse signal recovery.)
- 2011-04-01 17:08:36下载
- 积分：1
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DSp
Here i attach the MATLAB codings for dsp applications. with the help of these coding we can easily design the dsp applications
- 2013-07-30 14:03:53下载
- 积分：1
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Untitled2
f i g9 _ 9 . ma t. This file and the other magnetization curves in this chapter are
available for download from the book s World Wide Web site (see Preface for
details).
- 2012-11-29 05:27:33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
