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surfer8
Surfer是地学中常用的一个软件，可以完成等值线图，立体图，流向图等的汇制。其自带的类VBA的开发语言，可以进行二次开发。附件是一些源程序。其中的Jackknifes可以用来对比不同的内差方法，从而得出最优化的内差方法。
(Surfer is the study of a commonly used software, complete contour map, a three-dimensional map, map the flow of the system. Its built-in categories VBA programming languages that can be re-developed. Annex some source. Jackknifes which can be used for comparison within the poor, so as to come up with the most optimal way worse.)
- 2007-07-03 00:30:05下载
- 积分：1
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hough
用hough监测圆................(fdfddffasdghgfsf)
- 2009-03-20 16:10:39下载
- 积分：1
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leach_mod1
leach协议在matlab改进。对于初学者matlab了解leach协议有很好的帮助。(leach xie yi in the MATLAB)
- 2010-05-10 11:15:04下载
- 积分：1
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Grey-Neural-Network
基于灰色神经网络的预测算法—订单需求预测(Grey Neural Network Based Prediction Algorithm- order demand forecast)
- 2011-05-23 18:36:10下载
- 积分：1
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808823291readdem
dem模型读写(read dem)
- 2014-12-21 21:37:55下载
- 积分：1
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fir_filter
LOW pass FIR filter for multirate processing
- 2015-02-09 09:59:02下载
- 积分：1
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B-spline
均匀三次B样条曲线插值，定义插值函数，B样条拟合三维曲面(Interpolation of B spline curves)
- 2017-12-24 11:04:55下载
- 积分：1
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dsc-single-sideband-modulation
说明： dsc单边带调制。功能是用10Hz的载波信号去调制频率为500Hz的基带信号，然后得到单边带调制信号(single-sideband modulation)
- 2011-04-01 10:15:56下载
- 积分：1
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erweibodong
维波动显格式计算程序，本程序采用有限差分法来计算二维波动问题(Two-dimensional wave the explicit calculation program, this program USES the finite difference method to calculate the two-dimensional wave problem
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- 2015-03-21 13:57:13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
