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sourcecode
《MATLAB实用教程》 MATLAB例程源代码 可作为电子、通信、自控等专业本科生研究生及广大科研人员的参考。
(" MATLAB Practical Tutorial" MATLAB routine source code can be used as electronics, communications, self-control and other professional researchers undergraduate students and the general reference.)
- 2010-03-01 01:58:49下载
- 积分：1
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Vibration-frequency-
工程中，振动信号有时域与频域两种分析方法，该文件是频域分析处理方法(Engineering, vibration signal and frequency domains sometimes two analysis methods, the document is a frequency domain analysis and processing methods)
- 2013-06-03 18:59:14下载
- 积分：1
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LMIinMATLAB
说明： LMI 在MATLAB的應用 幫助初學者進入LMI的世界(LMI in the application of MATLAB)
- 2009-08-04 10:27:42下载
- 积分：1
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code
有限元求解结构固有频率和振型、显示（中心差分法，Newmark法）、隐式法、非线性法程序(middle difference/Newmark/nonlinear)
- 2020-10-05 11:17:38下载
- 积分：1
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ellipse
matlab的函数ellipse.m，来自美国的，用以根据给出的参数分析画出椭圆(The matlab function ellipse.m, from the United States, is used to draw ellipses based on the given parameter analysis.)
- 2018-05-10 12:42:33下载
- 积分：1
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singleNPC
实现三电平整流功能，相对于两电平，电平数更多，谐波更小(Achieve three-level rectification function, with more levels and less harmonics than two levels)
- 2019-05-23 10:43:40下载
- 积分：1
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compressorINT16clip2
模拟语音信号的动态范围压缩算法的matlab源码(Yiu Pang音portability cavity ulcers信rabble-hung Yu Yang围压stand for goblets算Daitou tungsten cavity matlab)
- 2007-08-23 11:32:04下载
- 积分：1
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AP
说明： 对数值进行分析，AP聚类算法是基于数据点间的"信息传递"的一种聚类算法。与k-均值算法或k中心点算法不同，AP算法不需要在运行算法之前确定聚类的个数。AP算法寻找的"examplars"即聚类中心点是数据集合中实际存在的点，作为每类的代表(For numerical analysis, AP clustering algorithm is based on the "information transfer" between data points. Unlike k-means algorithm or k-center algorithm, AP algorithm does not need to determine the number of clusters before running the algorithm. The "examples" searched by AP algorithm is the actual points in the data set as the representative of each class)
- 2021-01-07 11:03:26下载
- 积分：1
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test-time-of-program
测试matlab的程序时间，可以对比各种算法的执行效率，实用性很强哦(test time of program)
- 2011-05-29 08:18:40下载
- 积分：1
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PSOGSA_v3
This is a Hybrid PSO GSA Aigorithm.A new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms’ strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution.
- 2014-02-11 14:45:09下载
- 积分：1
