-
Matlab
这是一个matlab图像处理函数汇总，本人感觉很好，到时可以查就了然于心(This is a summary of matlab image processing function, I feel good, to the clear understanding can check on the heart)
- 2010-09-02 15:45:35下载
- 积分：1
-
PSCADexamples
pscad的具体实例，适合初学者，能尽快熟悉pscad的运行等。(pscad examples)
- 2010-05-31 13:12:28下载
- 积分：1
-
Frequency-Response-Plotter-Code_matlab
Once you input filter coeffiecient you can get frequency responce of filter and drawing that shows the position of pole, zero.
- 2012-07-29 15:24:42下载
- 积分：1
-
opf
系统的结构参数和负荷情况都已给定时，调节可利用的控制变量(如发电机输出功率、可调变压器抽头等)来找到能满足所有运行约束条件的，并使系统的某一性能指标(如发电成本或网络损耗)达到最优值下的潮流分布。(Optimal power flow(OPF) is a fundamental tool in power system planning and operation.)
- 2021-01-25 16:28:43下载
- 积分：1
-
Introduction-of-wavelet-analysis
关于傅里叶变换和小波分析的基础知识内容。并附带基于Matlab案例进行说明。(Basic konwledge for Fourier transform and wavelet analysis with examples written in matlab )
- 2013-12-17 09:46:01下载
- 积分：1
-
SYM1
说明： GM（1，1）灰色预测模型预测发展趋势，并确定模型的精确合格度(GM (1,1) gray prediction model predicts the development trend and determines the accuracy of the model)
- 2020-02-17 22:59:55下载
- 积分：1
-
lorens
说明： 关于Lorenz函数处于混沌时的图形，包括建立Lorenz函数集画图程序(about Lorenz)
- 2010-04-03 18:48:13下载
- 积分：1
-
wave_module_maximum
模极大程序，小波模极大程序。模极大程序，小波模极大程序模极大程序，小波模极大程序模极大程序，小波模极大程序(Modulus maxima procedure, wavelet modulus maxima procedures modulus maxima procedure on modulus maxima of wavelet modulus maxima procedure, wavelet modulus great program)
- 2012-04-29 10:30:17下载
- 积分：1
-
BVP_tutorial
求解常微分方程组的bvp解算器的使用说明和教程和实例(tutorial and documentary and examples for ODE bvp)
- 2013-11-02 00:06:12下载
- 积分：1
-
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
