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matlab-distrib-1
matlab code using in mathematical optimization
- 2010-10-25 16:09:18下载
- 积分：1
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LineRANSAC
实现利用RANSAC方法对输入图像的线特征检测的功能。(RANSAC method to achieve the input image using the line feature detection function.)
- 2011-01-15 19:24:29下载
- 积分：1
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AHP
层次分析方法，用于有多个方案和多个准则的情况下，选择出其中的最优方案(AHP, for there are more programs and a number of criteria in the case, choose the optimal solution which)
- 2011-07-18 13:47:23下载
- 积分：1
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Stochastic-Lab
stochastic lab matlab code such as guassian process
- 2015-01-19 17:15:35下载
- 积分：1
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含有噪声的飞行轨迹及数据处理
说明： 用于生成带噪声的无人机飞行轨迹，同时通过中值和小波滤波方式进行噪声去除(It is used to generate the flight path of UAV with noise and remove the noise by means of median and wavelet filtering)
- 2020-01-13 15:15:37下载
- 积分：1
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solarpanel
modeling and simulation of photovoltaic systems with matlab simulink
- 2011-03-31 05:15:19下载
- 积分：1
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untitled0
一个简单实用的二阶多智能体系统一致性的simulink仿真框图，可以进行修改，做实验等等(A simple and practical second-order multi-agent system consistency simulink simulation block diagram can be modified to do experiments, etc.)
- 2013-01-27 18:40:49下载
- 积分：1
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monteCarloOptimLeverage
monte carlo optimal leverage calculation algorithm in matlab on audcad fx currency pair
- 2014-09-12 00:14:27下载
- 积分：1
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Iterative-Algorithms--
本论文从CT图像重建原理入手, 根据迭代重建的物理意义, 从投影模型出发, 得到投影数据, 然后利用迭代算法MART 及SIRT 重建出断层图像, 再与模型相比较, 从而确定各算法的优劣, 总结出如何选择合适的图像重建算法。(Iterative Algorithms in Computed Tomographic Reconstruction)
- 2015-12-02 16:48:58下载
- 积分：1
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RBF为径向基函数，RBF网络把网络看作对未知函数的逼近器
说明： RBF为径向基函数，RBF网络把网络看作对未知函数的逼近器。输入信号为正弦信号u(k)=0.35sin(3*pi*t)，采用时间为0.001s,网络隐层神经元个数取m=4，网络结构为输入层2-隐层4-输出1，网络的出事全职取随机值，高斯函数的初始值取Cj=[0.65,0.65]T,B=[1.35,1.35,1.35,1.35]T,网络的学习参数取a=0.07,n=0.9。(RBF is a radial basis function, and RBF network regards the network as an approximator to the unknown function. The input signal is sinusoidal signal u(k)=0.35 sin(3*pi*t), the time is 0.001s, the number of hidden layer neurons is m=4, the network structure is input layer 2-hidden layer 4-output 1, the network accident takes random value full-time, the initial value of Gauss function takes Cj=[0.65, 0.65] T, B=[1.35, 1.35] T, the learning parameters of the network take a=0.07, n=0.9.)
- 2019-01-06 19:55:58下载
- 积分：1
