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在“
Elsevier电子期刊
”中,
命中:
160
条,耗时:0.0179917 秒
在所有数据库中总计命中:
289
条
1.
Regression
and Kriging metamodels with their experimental designs in simulation: A review
作者:
Jack P.C. Kleijnen
;
kleijnen@tilburguniversity.edu" class="auth_mail" title="E-mail the corresponding author
关键词:
Robustness and sensitivity
;
Metamodel
;
Design
;
Regression
;
Kriging
刊名:European Journal of Operational Research
出版年:2017
2.
Efficient estimation of nonstationary factor models
作者:
In Choi
inchoi@google.com
inchoi@sogang.ac.kr
关键词:
Factor model
;
Unit root
;
Generalized principal component estimation
;
Feasible generalized principal component estimation
刊名:Journal of Statistical Planning and Inference
出版年:2017
3.
Optimal octane number correlations for mixtures of toluene reference fuels (TRFs) and ethanol
作者:
Hao Yuan
a
;
Yi Yang
a
;
yi.yang@unimelb.edu.au" class="auth_mail" title="E-mail the corresponding author
;
Michael J. Brear
a
;
Tien Mun Foong
a
;
James E. Anderson
b
关键词:
Octane number
;
Correlation
;
Gasoline surrogate
;
Toluene reference fuel
;
Ethanol
刊名:Fuel
出版年:2017
4.
A fuzzy
regression
approach using Bernstein
polynomials
for the spreads: Computational aspects and applications to economic models
作者:
Antonio Francisco Roldá
;
n Ló
;
pez de Hierro
a
;
aroldan@ugr.es" class="auth_mail" title="E-mail the corresponding author
;
Juan Martí
;
nez-Moreno
b
;
jmmoreno@ujaen.es" class="auth_mail" title="E-mail the corresponding author
;
Concepció
;
n Aguilar Peñ
;
a
c
;
caguilar@ujaen.es" class="auth_mail" title="E-mail the corresponding author
;
Concepció
;
n Roldá
;
n Ló
;
pez de Hierro
d
;
iroldan@ugr.es" class="auth_mail" title="E-mail the corresponding author
关键词:
Fuzzy
regression
;
Fuzzy number
;
Bernstein
polynomials
;
Goodness-of-fit
刊名:Mathematics and Computers in Simulation
出版年:2016
5.
Investigation of using robust statistics: Weighted, parametric & non parametric linear
regression
of derivative/discrete Fourier transform HPLC convoluted peak responses for handling standard addition method data in bioanalysis
作者:
Mohamed A. Korany
;
makorany@yahoo.com" class="auth_mail" title="E-mail the corresponding author
;
Heba H. Abdine
;
Marwa A.A. Ragab
;
Sara I. Aborass
关键词:
Standard addition method
;
Robust
regression
methods
;
Weighted
regression
;
Convoluted HPLC derivative curves
;
Ceftazidime elimination in urine
刊名:Bulletin of Faculty of Pharmacy, Cairo University
出版年:2016
6.
Combination of B-Spline and Legendre functions in random
regression
models to fit growth curve of Moghani sheep
作者:
P. Zamani
;
pzamani@basu.ac.ir
;
M.R. Moradi
;
D. Alipour
;
Farhad Ghafouri-Kesbi
关键词:
Sheep
;
Growth
;
Random
regression
models
;
B-Splines
;
Legendre
刊名:Small Ruminant Research
出版年:2016
7.
Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation
作者:
Kunkun Tang
a
;
b
;
ktg@illinois.edu" class="auth_mail" title="E-mail the corresponding author
;
Pietro M. Congedo
b
;
Ré
;
mi Abgrall
c
关键词:
Uncertainty quantification
;
Non-intrusive approach
;
Global sensitivity analysis
;
Analysis of Variance (ANOVA)
;
Polynomial dimensional decomposition (PDD)
;
Regression
approach
;
Adaptive sparse polynomial surrogate model
;
Atmospheric reentry
刊名:Journal of Computational Physics
出版年:2016
8.
Comparative Research on Breeding Value Prediction for Milk Yield in Cattle
作者:
Mircea-Cătălin Rotar
;
rotar.mircea.catalin@gmail.com" class="auth_mail" title="E-mail the corresponding author
;
Horia Grosu
;
Mihail-Alexandru Gras
;
Rodica-Ştefania Pelmuş
;
Cristina Lazăr
关键词:
test-day random
regression
;
Legendre
Polynomials
;
Wood
;
Ali&Schaeffer
;
P-Spline.
刊名:Agriculture and Agricultural Science Procedia
出版年:2016
9.
Amp: A modular approach to machine learning in atomistic simulations
作者:
Alireza Khorshidi
;
Andrew A. Peterson
;
andrew_peterson@brown.edu" class="auth_mail" title="E-mail the corresponding author
关键词:
Potential energy surface
;
Neural networks
;
Atomic Simulation Environment (ASE)
;
Density functional theory
;
Zernike
polynomials
刊名:Computer Physics Communications
出版年:2016
10.
Constructing general partial differential equations using polynomial and neural networks
作者:
Ladislav Zjavka
a
;
lzjavka@gmail.com" class="auth_mail" title="E-mail the corresponding author
;
Witold Pedrycz
b
;
c
;
d
;
wpedrycz@ualberta.ca" class="auth_mail" title="E-mail the corresponding author
关键词:
General partial differential equation composition
;
Multi-variable function approximation
;
Differential polynomial neural network
;
Sum derivative term substitution
刊名:Neural Networks
出版年:2016
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