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MUSIC1
模拟2个独立窄带信号分别以20°,60°的方向入射到均匀线阵上,信号间互不相关,与噪声相互独立,噪声为理想高斯白噪声,阵元间距为入射信号波长的1/2,信噪比为20dB,阵元数为10,采样快拍次数为200。(Simulate two independent narrowband signals in the direction of 20 °, 60 ° of the incident on the ULA, among signal unrelated, independent noise, the noise is ideal Gaussian white noise, the array element spacing of the incident signal wavelength of 1/2, the signal to noise ratio is 20dB, the array element number 10, the sampling frequency of 200 snapshots.)
- 2014-12-02 10:47:55下载
- 积分:1
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cyclostationary_toolbox
循环自相关函数工具箱,还带有自己编写的代码,ex1-7.(Circulating auto-correlation function of the toolbox, but also prepared with its own code, ex1-7.)
- 2021-03-11 10:19:26下载
- 积分:1
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mutual_information
在相空间重构中,用互信息法求最小关联嵌入维(In phase space reconstruction, the use of mutual information method associated minimum embedding dimension)
- 2008-05-05 09:21:23下载
- 积分:1
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rough-set
粗糙集理论是一种基于对现实世界进行不确定描述的理论,属性约简是粗糙集理论的核心理论之一。对粗糙集理论进行阐述,并给出基于启发式的知识约简算法,最后结合MATLAB程序实例说明了该算法的可行性与有效性。(Rough set theory is based on the uncertainty of the real world is described in theory, attribute reduction is the core theory of rough set theory one. Elaborate on the rough set theory, and gives the knowledge-based heuristic reduction algorithm, and finally with MATLAB program example illustrates the feasibility and effectiveness of the algorithm.)
- 2011-04-19 10:10:28下载
- 积分:1
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Very_Innovative
Numerical Solution, finite difference method, 3D, GUI, A very nice example of GUI
- 2014-12-25 13:43:36下载
- 积分:1
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chardiv
说明: 利用BP神经网络进行字符识别的系统源码,能进行有效数字字符识别。(BP neural network Character Recognition system source code, can be effective digital character recognition.)
- 2006-02-28 19:19:23下载
- 积分:1
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invLeastSquaresWe
线性反演的最小方差反演算法 MATLAB程序(Minimum variance of the linear inversion inversion algorithm MATLAB program)
- 2012-05-25 09:16:27下载
- 积分:1
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matlabGPS
matlab仿真,关于gps的,一些代码(failed to translate)
- 2013-05-04 20:08:33下载
- 积分:1
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Allpass_filters
It calculates a Thiran filter for fractional delay
- 2009-10-24 20:22:40下载
- 积分:1
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nueralNetwork
It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described
- 2013-08-07 18:58:45下载
- 积分:1