-
simulink
说明： 首先介绍计算机仿真技术和仿真建模方法的基本概念，以便对建模和仿真有个初步和整体的认识；然后对 Simulink 进行简单介绍，并以一个简单例子进行引导；最后介绍 Simulink的工作原理，为后续的深入掌握 Simulink 打下基础。(First introduces the computer simulation technology and the basic concepts of simulation modeling methods, modeling and simulation in order to have a preliminary and general understanding and then a brief introduction to the Simulink and be guided by a simple example Finally, the working principle of Simulink, Simulink for subsequent understanding of the basis.)
- 2011-03-18 20:32:16下载
- 积分：1
-
MLP
Online identify the plant dynamic by using intelligent architectures here using MLP architecture neural network for the plant.
- 2010-05-22 23:04:31下载
- 积分：1
-
qpskmod
QPSK modulation in Rayleigh channel
- 2013-09-19 20:32:55下载
- 积分：1
-
20103112_homework2
Add, remove, subtract, attach polynomials
- 2015-01-13 15:33:46下载
- 积分：1
-
ga遗传算法工具箱求解0-1整数规划问题
说明： 遗传算法 matlab(matlab ga)
- 2019-04-02 09:43:46下载
- 积分：1
-
slmpapr2
papr ofdm slm .it contain report and smatlab simulations.all are working ofdm slm method.slm method
- 2010-06-05 01:03:30下载
- 积分：1
-
P_out_S
带交叉电极的混频器中，电极的面积对THz输出功率的影响(Mixer with cross-electrode, the electrode area, the impact of the THz output power)
- 2011-09-18 15:12:25下载
- 积分：1
-
neidianfa-MATLAB
内点法matlab程序，其中包含对经济性要求条件(Interior point method matlab program, which contains the conditions of the economic requirements)
- 2013-02-28 15:05:57下载
- 积分：1
-
ScopeMath_v2.5
ScopeMath_v2.5 - Matlab Libraries for Scope
- 2014-08-31 21:47:18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
