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chap06
这个是基于matlab的二次型的最优化问题的课件，值得一看。(This is based on the matlab quadratic optimization problem of the courseware, worth a visit.)
- 2007-08-07 10:31:12下载
- 积分：1
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obj_display
读取obj模型并且显示的matlab代码。
简单易懂(matlab code to read .obj model)
- 2011-01-15 16:02:43下载
- 积分：1
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matlab
该电子书用于初学者的学习软件开发和应用,使初学者比较快速的学习MATLAB(book for beginners learning software development and application of)
- 2010-03-04 17:10:25下载
- 积分：1
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threshold_tr
Training of the basic linear classifier where seperation hyperplane
is perpedicular to one dimension.
- 2011-01-23 18:20:28下载
- 积分：1
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S2
说明： mfile in matlab about jame machin elec
- 2013-08-21 15:55:56下载
- 积分：1
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s_heli_2d_ff_lqr_i
这是关于二自由度直升机模型的前向控制和反馈控制的simulink仿真模型(This is the simulink simulation model helicopter model before about two degrees of freedom to control and feedback control)
- 2013-11-28 21:19:41下载
- 积分：1
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GP-MATLAB
一个遗传规划的源程序，matlab语言编程(A source of genetic programming, matlab language programming)
- 2021-04-12 10:28:58下载
- 积分：1
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Matlab05
以Rossler方程!Duffing方程和Vanderpol方程为例,应用Matlab仿真工具进行模拟,
并对仿真结果作了简要说明和讨论,其中的示例对混沌研究和教学有一定的意义.(To Rossler equation! Duffing equation and the van der Pol equation as an example, the application of Matlab simulation tool for simulation, simulation results and a brief description and discussion, including examples of research and teaching on the chaos has a certain significance.)
- 2008-05-16 10:46:42下载
- 积分：1
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parallel_RLC_circuit_Matlab_simulink
Matlab code and Simulink model for parallel RLC circuit with different input functions
- 2014-02-07 07:23:53下载
- 积分：1
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1807.01622
说明： 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
