搜索资源列表
lhs-prcc
- 提供多种函数来计算perform uncertainty and sensitivity analysis，并用散点图和柱状图来显示计算结果。-We implemented many scr ipts and functions to perform uncertainty and sensitivity analysis (for PRCC and eFAST) and display scatter plots (for sample-based methods only). The scr
generar_LHS_Choleskwewey
- Stochastic fields Generation. created by LHS. Correlation is based on Cholesky factorization
manish
- [mimi,i]=min(abs(imag(disp1)-imag(disp2))) legend( LHS of dispersion relation , RHS of dispersion equation ) xlabel( u,cl ) figure(1) clc uppercuts = input( Enter number of intersection points desired: ) [g,h] = ginput(uppercut
lhs-prcc
- sensitivity calculus
lhs
- PEST Utilities to complement Latin Hypercube Sampling Software developed by Sandia National Laboratories -PEST Utilities to complement Latin Hypercube Sampling Software developed by Sandia National Laboratories
generar LHS_Cholesky
- 基于 Cholesky相关系数，数据随机产生的拉丁方抽样程序代码(Stochastic fields Generation. created by LHS. Correlation is based on Cholesky factorization)
modify_surrogate
- 拉丁超立方抽样及BP神经网络代理模型的建立与预测误差分析(Latin hypercube sampling & BP neural network model)
Latin Hypercube Sampling
- 这是从多元正态分布、均匀分布和经验分布中实现拉丁超立方体采样的采样实用程序。变量之间的相关性可以被描述出来。(This is sampling utility implementing Latin hypercube sampling from multivariate normal, uniform & empirical distribution. Correlation among variables can be sprecified)
LHS-Kriging
- 正态分布、均匀分布拉丁超立方抽样，Kriging模型(LHS,Kriging,latin_hs,lhsu)
e702633d
- 拉丁超立方抽样（英语：Latin hypercube sampling，缩写LHS）是一种从多元参数分布中近似随机抽样的方法，属于分层抽样技术，常用于计算机实验或蒙特卡洛积分等。(Latin hypercube sampling (abbreviated LHS) is an approximate random sampling method from multivariate parameter distribution. It belongs to stratified sampling
lhs
- 拉丁超立方抽样技术最早于1979年由McKay等提出，该方法具有以下优点： 具有均匀分层的特性 可以在较少抽样的情况下，得到尾部的样本值 以上两点使得拉丁超立方抽样比起普通的抽样方法更加的高效。 首先确定样本数N，既要抽取的样本数目 将（0，1）区间均分为N段 在这N段中的每一段随机的抽取一个值 将抽取的值通过标准正态分布的反函数映射为标准正态分布样本 打乱抽样顺序，用matlab中的sort函数