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matlab6.5handbook
有关MATLAB的技术手册，可以方便大家查阅学习软件(about matlab handbook)
- 2009-03-09 09:37:15下载
- 积分：1
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Kalman_Fliter_1960
本文来自卡尔曼发表的一篇论文，分析维纳滤波不足的基础上，发表的最优估计算法(This article from the Kalman published a paper, analysis based on the lack of Wiener filtering, published in the optimal estimation algorithm)
- 2010-03-07 10:20:25下载
- 积分：1
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tqjs
说明： 实现大地测量学中，椭球参数解算。（包括正解和反解）(Achieve geodesy, the ellipsoid parameters solver. (Including the positive solution and counter-solution))
- 2010-03-30 22:11:08下载
- 积分：1
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Doc1
this is new approach for fuel cell control.it is really helpfull.
- 2013-09-09 17:12:27下载
- 积分：1
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MATLAB-programs
戴维.鲁伯特的统计与金融一书的matlab代码( matlab code of<statistics and finance >by David Rupert )
- 2013-11-02 13:02:53下载
- 积分：1
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MATLABProgramSourceCode
这是科学运算语言MATLAB程序设计有关的函数代码(This is a scientific computing language of MATLAB programming code related to the function)
- 2010-07-08 10:00:24下载
- 积分：1
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channel_estimation
this matlab code about estimation channel
- 2011-11-04 01:00:07下载
- 积分：1
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MMSE-Simulation
一个关于MMSE算法的仿真实现，是时域均衡MMSE仿真的实用教程(A simulation of MMSE algorithm implementation, it is time domain equalization MMSE simulation practical tutorial)
- 2014-02-10 18:04:59下载
- 积分：1
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example_of_particle_filter
一个粒子滤波简单的例子。运行方法：解压之后，放到工作目录，运行PF_ex1.m(a simple example of particle filter.)
- 2015-04-19 10:51:35下载
- 积分：1
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gpml-matlab-v1.3-2006-09-08
说明： 高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)
- 2020-02-26 20:39:48下载
- 积分：1
