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stanford_convex_optimization_book
国外的经典的有关于凸优化数学方面的教材，值得研究有关优化方面的研究者学习(Classic abroad on convex optimization mathematics textbooks, it is worth to study the optimization of the researchers studying)
- 2010-06-12 13:35:08下载
- 积分：1
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root
Chapter 2. The Solution of Nonlinear Equations f(x) = 0
Algorithm 2.1 Fixed Point Iteration
Algorithm 2.2 Bisection Method
Algorithm 2.3 False position or Regula Falsi Method
Algorithm 2.4 Approximate Location of Roots
Algorithm 2.5 Newton-Raphson Iteration
Algorithm 2.6 Secant Method
Algorithm 2.7 Steffensen s Acceleration
Algorithm 2.8 Muller s Method
Algorithm 2.9 Nonlinear Seidel Iteration
Algorithm 2.10 Newton-Raphson Method in 2-Dimensions
- 2011-06-07 03:04:18下载
- 积分：1
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shuzhifenxi
matlab的数值分析，利用matlab来科学计算。用超松弛法求解Ax=b。(Matlab numerical analysis using matlab to scientific computing. Overrelaxation method for solving Ax = b.)
- 2012-06-17 19:41:40下载
- 积分：1
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泊松方程.tar
计算泊松方程的方程程序算法。计算物理的非常经典的算法之一。(An algorithm for calculating the Poisson equation. One of the very classic algorithms for computing physics)
- 2018-04-01 16:18:44下载
- 积分：1
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13 Markov链
说明： 马尔可夫链可被应用于蒙特卡罗方法中，形成马尔可夫链蒙特卡罗（Markov Chain Monte Carlo, MCMC） ，也被用于动力系统、化学反应、排队论、市场行为和信息检索的数学建模。(Markov Chain Monte Carlo, MCMC)
- 2020-10-18 16:07:26下载
- 积分：1
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featureselection
基因算法实现的特征提取，实现平台是matlab(feature selection with genetic algorithm)
- 2009-02-18 19:52:53下载
- 积分：1
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1
说明： LMS在加性高斯白噪声信道下迭代2000步的输出能量和误差权矢量(LMS in the additive white Gaussian noise channel output of the next iteration step 2000, the energy and the error weight vector)
- 2009-11-15 16:46:54下载
- 积分：1
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clustering-based-on-improved-ACO
给出了一种改进的蚁群算法的聚类程序，比基本蚁群聚类效果好。(very useful)
- 2011-06-09 21:38:00下载
- 积分：1
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music(doa)
七单元天线阵MUSIC DOA估计：
d=1 ， 天线阵元的间距；
lma=2， 信号中心波长；
四输入信号；
A=[A1,A2,A3,A4]， 得出A矩；
四信号的频率d=[1.3*cos(v1*n)
1*sin(v2*n)
1*sin(v3*n)
1*sin(v4*n)]
构造输入信号矢量
U=A*d
总的输入信号
总输入信号的协方差矩阵
[s,h]=eig(c)
求协方差的特征矢量及特征值
取出与零特征值对应的特征矢量
求协方差矩阵的逆矩阵
应用Music法估计输出
绘出各波达方向图(Seven-element antenna array MUSIC DOA estimates: d = 1, Antenna Array pitch LMA = 2 signal center wavelength four input signals A = [A1, A2, A3, and A4], drawn A moment tetra-frequency of the signal D = [1.3* cos (V1* n) 1* sin (v2* n) 1* sin (v3* n) 1* sin (V4* n)] constructed input signal vector U = A* D of the total input signal of the total input signal covariance matrix [S] = EIG (c) seeking covariance feature vector and the feature value removing and corresponding to the zero eigenvalues characterized vector seeking covariance matrix inverse matrix Applications Music estimate output plotted DOA Figure)
- 2013-04-15 23:48:49下载
- 积分：1
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noyaau
Implémentaion method of kernal
- 2011-12-01 16:49:32下载
- 积分：1
