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pde
Matlab 官方工具箱使用说明
pde偏微分方程工具箱.pdf(Matlab toolbox official use of partial differential equations PDE toolbox. Pdf)
- 2008-01-26 17:35:55下载
- 积分：1
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chapter1
说明： 相關於數位信號處理利用MATLAB應用的算法集(Algorithm Collections for Digital Signal Processing Applications Using MATLAB)
- 2010-04-21 14:29:03下载
- 积分：1
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pv_module
A pv module is modeled in MATLAB/SIMULINK environment with use of mathematical equations that describe pv module s schematic model.
This model has 2 inputs named Vpv and insolation and has 2 outputs named Ipv and Ppv.
Insolation signal is varying.
- 2013-10-17 03:11:58下载
- 积分：1
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6Pmethods
自适应波束形成多种算法的matlab实现，算法类型：MVDR,LCEC,GSC,PCI,MWF,PCA-MVB,SC-MVB,EC(A variety of adaptive beamforming algorithm matlab implementation, the algorithm type: MVDR, LCEC, GSC, PCI, MWF, PCA-MVB, SC-MVB, EC)
- 2011-05-09 15:11:59下载
- 积分：1
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MATLAB
光学matlab仿真： 平面波和球面波干涉(Optical matlab simulation: a plane wave and spherical wave interference)
- 2021-03-10 22:09:26下载
- 积分：1
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PSO-TSP
通过matlab运用粒子群算法（PSO）解决TSP51个城市问题求解(Using particle swarm optimization algorithm (PSO) to solve the problem of solving TSP51 urban problems by Matlab)
- 2021-03-30 09:59:10下载
- 积分：1
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prom_3
最小方差调节器和最小方差自校正调节器。
设计最小方差调节器和最小方差自校正调节器，实现闭环仿真控制，了解这两种调节器的性质，特别是某些参数(如遗忘因子)的影响(Minimum variance regulator and the minimum variance self-tuning regulator. Design of minimum variance and minimum variance regulator self-tuning regulator to achieve closed-loop simulation of control, to understand the nature of the two regulators, in particular some of the parameters (such as forgetting factor) of)
- 2010-09-03 14:28:34下载
- 积分：1
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CurvilinearRegressionAnalysing
曲线回归分析：直线回归、双对数回归、半对数回归、倒数回归(Curvilinear regression analysis: linear regression, double logarithmic regression, semi-logarithmic regression, countdown)
- 2009-12-31 22:10:23下载
- 积分：1
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MIMOCDMA
a MIMO CDMA matlab code
- 2010-07-14 15:41:07下载
- 积分：1
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disteu
DISTEU Function
DISTEU Pairwise Euclidean distances between columns of two matrices
Input:
x, y: Two matrices whose each column is an a vector data.
Output:
d: Element d(i,j) will be the Euclidean distance between two
column vectors X(:,i) and Y(:,j)
Note:
The Euclidean distance D between two vectors X and Y is:
D = sum((x-y).^2).^0.5
- 2013-03-15 15:11:01下载
- 积分：1
