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ZHUANGTAI
说明： 状态空间模型 加强ＭＡＴＬＡＢ学习 很有用的
(state-space model to strengthen MATLAB very useful learning)
- 2006-04-28 13:07:29下载
- 积分：1
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yuyinjiance
语音的端点检测是根据语音的特征来进行了，本文根据语音不同帧之间能量的不同，一帧一帧的进行比较(Speech endpoint detection is based on the characteristics of voice, different voice between the different frames of energy, a one to compare)
- 2012-05-30 20:24:09下载
- 积分：1
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splinefit
matlab code for SPLINEFIT
- 2015-02-07 13:38:22下载
- 积分：1
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Spread_Frequency
TD中扩频的链路级仿真
通过MATLAB实现(TD Spreading the Link Simulation through MATLAB)
- 2007-04-18 14:19:23下载
- 积分：1
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MOGA
这是一个简单的用matlab编写的多目标遗传算法，可以供初学者参考(This is a simple multi-objective genetic algorithm using matlab, you can reference for beginners)
- 2013-04-04 12:09:30下载
- 积分：1
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matlab_example
过程辨识中，实现最小二乘法的仿真，并画出仿真的图行(a example of matlab)
- 2011-11-16 16:25:29下载
- 积分：1
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youhua
优化法求救方程最小值或最优解
matlab命令窗口会出现几个诸如以下结果的数据(Optimization method distress minimum or optimal solution equation matlab command window will appear as the following results in several data)
- 2009-02-25 08:55:35下载
- 积分：1
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wind_turbine_blade_coordinates_calculate
说明： 该code读出naca4412.txt中基本balde坐标，在读取data1.txt中设计要求，生成FinalCoordinates.txt文件，包含所有生成balde airfoil所需coordinates。(this matlab code read the blade coordinates in naca4412.txt, and then read the design requirements in data1.txt. Finally, it will generate the FinlCoordinates.txt which include all the coordinates of blade airfoil.)
- 2010-04-07 08:30:23下载
- 积分：1
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Lab13_MultArrays
Lab13_MultArrays 多维数组的使用，实现数组定位(Use Lab13_MultArrays multidimensional arrays, array positioned to achieve)
- 2014-01-06 09:13:28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
