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Matlab(GUI-about-filter)
数字滤波器的matlab实现以及GUI实现(Matlab digital filter implementation and GUI implementation)
- 2011-06-24 23:20:46下载
- 积分:1
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LMScmpRLS
说明: 基于lms算法和rls算法的比较,主要针对基本的算法性能,收敛速度进行比较。(comparison between lms and rls)
- 2010-05-01 13:02:01下载
- 积分:1
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10-Feedback-Linearization
this file is about feedback linearization method.
- 2014-10-06 13:45:44下载
- 积分:1
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maple
maple的入门级教程,maple的计算相比matlab更加简单也更容易上手(The entry-level tutorial maple, maple computing matlab simpler and easier to use compared to)
- 2014-01-13 10:26:06下载
- 积分:1
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无线传感器节点部署定位模糊聚类算法采用数量积求相似矩阵
无线传感器节点部署定位模糊聚类算法采用数量积求相似矩阵 输入样本矩阵(The localization algorithm of wireless sensor nodes is based on fuzzy clustering algorithm.)
- 2018-11-07 10:46:56下载
- 积分:1
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math
离散数学用matlab实现,包括手实例(about matlab,about sign)
- 2012-10-01 12:51:10下载
- 积分:1
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hangjun
对HARQ系统的吞吐量分析,GPS和INS组合导航程序,计算目标和海洋回波的功率谱密度。( HARQ throughput analysis of the system, GPS and INS navigation program, Calculating a target and ocean echo power spectral density.)
- 2016-05-23 13:25:08下载
- 积分:1
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遗传优化及其2种改进算法
说明: matlab编写的基本遗传算法、顺序选择遗传算法、大变异遗传算法的源码,直接可以运行(The source code of basic genetic algorithm, sequential selection genetic algorithm and large variation genetic algorithm compiled by Matlab can be run directly)
- 2021-04-06 14:39:03下载
- 积分:1
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Odefun5
间歇反应釜的matlab建模,M文件,c可自由改变(Modeling of batch reactor)
- 2013-12-16 20:15:44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