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matlabtupian
希望对大家有用，是关于matlab的视频(It is useful ,i think)
- 2009-12-15 17:15:56下载
- 积分：1
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RBF_matlab
用MATLAB编写的RBF神经网络程序，能够逼近任意的非线性函数，可以处理系统内的难以解析的规律性.(Using MATLAB RBF neural network program that can approximate any nonlinear function that can handle difficult analytic regularity within the system.)
- 2014-03-29 20:30:38下载
- 积分：1
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Practical-signal-theory
Practical signal theory
- 2013-07-24 22:07:41下载
- 积分：1
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mfbfinal
matched filter bound implementation in matlab for a certain channel responses in a noisy channel
- 2011-10-17 00:08:27下载
- 积分：1
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State_Order_change
如何在Simulink中改变其状态量的顺序的实例程序，主要用于控制系统的设计(Explores how to leverage the Structure with Time Simulink data format to alleviate ordering and index issues on inputs/outputs/states.)
- 2011-01-04 15:25:58下载
- 积分：1
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figure9
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 19:57:59下载
- 积分：1
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UltimatePvp
ultimate co pvp.. souce c#
- 2014-01-14 19:51:28下载
- 积分：1
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write_segy_file
说明： 在地球物理勘探中将原始的SEG-Y数据导入(In geophysical exploration will be the original SEG-Y data import)
- 2011-03-29 11:27:22下载
- 积分：1
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scott
Scott (1992) lead to better large sample performance of the histogram.
- 2010-02-02 04:27:32下载
- 积分：1
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2dgaussian
汽车高斯曲面拟合
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2程序，以适应到表面二维高斯：
子= A *的进出口（ -（（西为X0）^2/2/sigmax^2 +（艺Y0的）^2/2/sigmay^ 2）。。）+ b的
这些例程是自动在某种意义上说，他们并不需要出发对模型参数的猜测规范。
autoGaussianSurfML（十一，彝，子）适合通过对模型参数的最大似然（最小二乘）。它首先计算了该模型在许多可能的参数值，然后选择最佳质量设置和细化与lsqcurvefit它。
autoGaussianSurfGS（十一，彝，紫）的估计，通过指定数据的贝叶斯生成模型，然后采取通过从模型吉布斯抽样样本后ofthis模型参数。这种(Auto Gaussian Surface fit
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2 routines to fit a 2D Gaussian to a surface:
zi = a*exp(-((xi-x0).^2/2/sigmax^2+ (yi-y0).^2/2/sigmay^2))+ b
The routines are automatic in the sense that they do not require the specification of starting guesses for the model parameters.
autoGaussianSurfML(xi,yi,zi) fits the model parameters through maximum likelihood(least-squares). It first evaluates the quality of the model at many possible values of the parameters then chooses the best set and refines it with lsqcurvefit.
autoGaussianSurfGS(xi,yi,zi) estimates the model parameters by specifying a Bayesian generative model for the data, then taking samples from the posterior ofthis model through Gibbs sampling. This method is insensitive to local minimain posterior and gives meaningful error bars (Bayesian confidence intervals))
- 2011-05-23 10:36:52下载
- 积分：1
