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TrunkingEfficient
Calculate Trunking Efficiency with MatLab
- 2009-10-18 18:53:58下载
- 积分:1
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ppt
关于matlab学习的PPT,里面介绍了matlab的一些基本知识,对于初学者非常有用。(PPT on matlab learning, which introduced some basic knowledge of matlab is very useful for beginners.
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- 2012-03-22 11:04:36下载
- 积分:1
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Feature-point-extraction
数字 图像处理特征提取 非常详细,推荐学习下载(Feature point extraction with matlab performance)
- 2013-01-06 19:50:42下载
- 积分:1
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Radar_KalmanIMM6
matlab 编写的源程序交互多模算法,用于目标多机动蒙特卡罗法仿真跟踪滤波器
本人十分关注 机动目标。这个是利用交互多模算法,用于目标多机动假设运动情况下的蒙特卡罗法仿真跟踪滤波器。matlab 编写的源程序。
但是有一点小错误,如果您能修改,不胜感激。(matlab source code prepared by the interactive multi-mode algorithm, used to target many motor simulation Monte Carlo method I am very concerned about tracking filter for maneuvering target. This is the use of interactive multi-mode algorithm, used to target many of motor sport where the assumption of the Monte Carlo method simulation tracking filter. matlab source code prepared. But there is a small error, if you can modify, greatly appreciated.)
- 2009-03-19 20:07:35下载
- 积分:1
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光纤光栅法布里—珀罗腔 FBGFP
光纤光栅法布里—珀罗腔(FBG F-P)的matlab仿真程序。利用光纤光栅的矩阵传输特性。(Fiber Bragg Grating Fabry- Perot cavity (FBG FP) of matlab simulation program. Optical fiber grating transmission characteristics of the matrix.)
- 2021-05-16 15:30:02下载
- 积分:1
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kde-toolbox
matlab的核密度工具箱,非常实用,简单易用。(nuclear density matlab toolbox, very practical, easy to use.)
- 2013-07-29 19:02:13下载
- 积分:1
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6
本程序实现多模态故障检测与故障诊断,用的方法是LPP_GMM,GMM是用的FJ算法,效果很好(This procedure realize multimodal, fault detection and fault diagnosis method is LPP_GMM, GMM FJ algorithm is used, the effect is very good
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- 2013-08-31 11:11:56下载
- 积分:1
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figure11
相控阵雷达的MIMO的仿真,对于雷达中的仿真(MIMO simulation of phased array radar, radar simulation)
- 2013-04-15 19:58:31下载
- 积分:1
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zaosheng11
雷达高斯分布相干视频噪声和高斯分布中频噪声仿真(Coherent Radar Gaussian noise and Gaussian frequency video noise simulation)
- 2013-09-29 08:29:11下载
- 积分:1
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matlab
聚类算法,不是分类算法。分类算法是给一个数据,然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据,然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类,是事先给出原始数据所含的类数,然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn},这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)
- 2014-02-18 09:59:02下载
- 积分:1