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200715211954208
一个有关AT88SC102卡读写方面的资料，电气性能介绍，读写时序等等方面的内容(A card reader on AT88SC102 information, electrical performance, presentations, read and write timing, etc., the contents of the)
- 2009-11-11 23:40:08下载
- 积分：1
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matlab
MATLAB LESSONS FROM UNIVERSITY
- 2012-02-12 13:06:46下载
- 积分：1
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matlab_netcdf_5_0
读取Network Common Data Form (netCDF)数据(Read Network Common Data Form (netCDF) data)
- 2008-07-12 02:26:53下载
- 积分：1
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liegouzhuituzi51
数学实验中用matlab对猎狗追逐兔子进行的仿真和分析(Matlab to the hunting dog chasing rabbits simulation)
- 2012-05-11 10:46:02下载
- 积分：1
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chonggou
配电网重构，实现平衡负荷，降低配电网网损的功能。(Distribution network reconfiguration, balanced load, power distribution network loss reduction.
)
- 2021-02-26 01:39:38下载
- 积分：1
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VoronoiLimit
Matlab Introduction (Spanish)
- 2013-04-01 09:31:59下载
- 积分：1
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para
船舶参数横摇,参数横摇的3自由度模型，包括垂荡和纵摇作用(Parametric Rolling of ships, the 3 degree of freedom model of parametric rolling, including heave and pitch function
)
- 2020-09-02 16:38:08下载
- 积分：1
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base
1.分别在两个图中画出δ(n)和δ(n-2)，其中-10≤n≤10，画出U（n）其中-10≤n≤10。
2.分别在两个图中画出x1(n)=cos(2π×20×0.01×n)，0≤n≤10和x2(n)=(0.8)n，1≤n≤6。
3.由上一题，计算y(n)=x1(n)+x2(n)，并画图。(1. Draw the figure at two δ (n) and δ (n-2), where-10 ≤ n ≤ 10, draw the U (n) where-10 ≤ n ≤ 10. 2. Respectively in the two Draw the figure x1 (n) = cos (2π × 20 × 0.01 × n), 0 ≤ n ≤ 10 and x2 (n) = (0.8) n, 1 ≤ n ≤ 6. 3. from the previous question, calculate y (n) = x1 (n)+ x2 (n), and drawing.)
- 2010-11-26 16:34:06下载
- 积分：1
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VectorExtraction
Extracting Vectors from Arrays
- 2012-07-20 01:08:56下载
- 积分：1
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SRGTSToolbox
说明： SURROGATES工具箱是一个多维函数逼近和优化方法的通用MATLAB库。当前版本包括以下功能：
实验设计：中心复合设计，全因子设计，拉丁超立方体设计，D-optimal和maxmin设计。
代理：克里金法，多项式响应面，径向基神经网络和支持向量回归。
错误和交叉验证的分析：留一法和k折交叉验证，以及经典的错误分析（确定系数，标准误差;均方根误差等;）。
基于代理的优化：高效的全局优化（EGO）算法。
其他能力：通过安全裕度进行全局敏感性分析和保守替代。(SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and optimization methods. The current version includes the following capabilities:
Design of experiments: central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs.
Surrogates: kriging, polynomial response surface, radial basis neural network, and support vector regression.
Analysis of error and cross validation: leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others).
Surrogate-based optimization: efficient global optimization (EGO) algorithm.
Other capabilities: global sensitivity analysis and conservative surrogates via safety margin.)
- 2020-04-20 22:30:13下载
- 积分：1
