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dtw
dtw的matlab源码 经试验好用 在matlab下(dtw' s matlab source)
- 2010-09-09 20:11:05下载
- 积分：1
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Adaptive_median_filter_using_Embedded_MATLAB
Adaptive median filter using Embedded MATLAB
- 2008-08-14 19:38:30下载
- 积分：1
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JakesCodes
Jakes仿真建模的基本程序，主要就是模型的构建程序，MATLAB环境下进行，有一定参考价值(Jakes simulation modeling program, model building program MATLAB environment, has a certain reference value)
- 2013-04-10 12:57:51下载
- 积分：1
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Modbus_omron_pmcr
Omron plc modbus protocol macro for SCU communication cards
- 2013-05-22 14:25:37下载
- 积分：1
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ssvmtoolbox
此源码提供了ssvm的完整的matlab源代码，主要运用UD设计原理对参数搜索提高了效率。(This source provides a complete ssvm the matlab source code, the main design principle of the use of UD on the parameters of the search efficiency.)
- 2008-04-25 11:23:50下载
- 积分：1
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bianwei10_m
自己编写的matlab中编辑个人模块的S-函数方法，通过这个模块可以实现输入信号变为原来的10倍(Imagecut.rar matlab edit personal module function method of S- this module can be realized by the input signal into a 10 times
)
- 2011-06-03 16:48:11下载
- 积分：1
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aaq1
经典music算法对一维水声信号进行处理的matlab程序，多注解菜鸟版。(Classical music algorithm for one-dimensional underwater acoustic signal processing matlab program, to comment rookie version.)
- 2012-05-24 15:46:21下载
- 积分：1
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simple_dwt
A simple code for transform wavelet decomposition
- 2013-10-05 23:45:25下载
- 积分：1
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AdaptiveEqualizationRLS_ENEE634
this file explain hoew to simulate RLS EQUALIZER on matlab .
- 2015-02-07 02:01:46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
