设为首页
收藏本站
网站地图
|
English
|
公务邮箱
About the library
Background
History
Leadership
Organization
Readers' Guide
Opening Hours
Collections
Help Via Email
Publications
Electronic Information Resources
常用资源
电子图书
期刊论文
学位会议
外文资源
特色专题
内部出版物
Wiley电子期刊(3)
SpringerLink电子期刊(720)
NATURE电子期刊(1)
Elsevier电子期刊(1043)
Springer电子图书(4)
ProQuest学位论文(25)
GSW全文库(2)
ACS电子期刊(25)
在“
Elsevier电子期刊
”中,
命中:
1,043
条,耗时:0.0319823 秒
在所有数据库中总计命中:
1,823
条
1.
Luciferase-based bioassay for rapid pollutants detection and classification by means of
multilayer
artificial
neural
networks
作者:
Ivan A. Denisov
d.ivan.krsk@gmail.com
Author Vitae
关键词:
Bioluminescence
;
Luciferase
;
Bioassay
;
Artificial
neural
networks
;
Perceptron
;
Machine learning
刊名:Sensors and Actuators B: Chemical
出版年:2017
2.
Application of artificial
neural
networks
to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube
作者:
Anita Csá
;
brá
;
gi
a
;
csabragi.anita@gmail.com
;
csabragi.anita@gek.szie.hu
;
Sá
;
ndor Molná
;
r
a
;
molnar.sandor@gek.szie.hu
;
Pé
;
ter Tanos
a
;
tanospeter@gmail.com
;
Jó
;
zsef Ková
;
cs
b
;
kevesolt@gmail.com
关键词:
ANN
;
Artificial
Neural
Network
;
CA
;
CB
;
CC
;
CD
;
Combination A
;
B
;
C
;
D
;
DO
;
dissolved oxygen
;
EC
;
electrical conductivity
;
GRNN
;
General Regression
Neural
Network
;
hydro PP
;
hydro power plant
;
HNPP
;
Hungarian Nuclear Power Plant
;
IA
;
Willmott&rsquo
;
s index of agreement
;
MAE
;
mean absolute error
;
MLPNN
;
Multilayer
Perceptron
Neural
Network
;
MLR
;
Multivariate Linear Regression
;
R2
;
coefficient of determination
;
RBFNN
;
Radial Basis Function
Neural
Network
;
RF
;
runoff
;
rkm
;
river kilometres
;
RMSE
;
r
刊名:Ecological Engineering
出版年:2017
3.
Comparison of linear regression and artificial
neural
networks
models to predict heating and cooling energy demand, energy consumption and CO
2
emissions
作者:
Rafael Pino-Mejí
;
as
a
;
rafaelp@us.es
;
Alexis Pé
;
rez-Fargallo
b
;
aperezf@ubiobio.cl
;
Carlos Rubio-Bellido
c
;
carlosrubio@us.es
;
Jesú
;
s A. Pulido-Arcas
d
;
jpulido@ubiobio.cl
关键词:
Neural
networks
;
Energy demand
;
Energy consumption
;
CO2 emissions
;
Energy efficiency
刊名:Energy
出版年:2017
4.
Hybrid integration of
Multilayer
Perceptron
Neural
Networks
and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS
作者:
Binh Thai Pham
a
;
b
;
phambinhgtvt@gmail.com
;
binhpt@utt.edu.vn
;
Dieu Tien Bui
c
;
Indra Prakash
d
;
M.B. Dholakia
e
关键词:
Landslides
;
Ensemble techniques
;
Multilayer
Perceptron
Neural
Network
;
Himalaya
;
India
刊名:CATENA
出版年:2017
5.
Application of Artificial
Neural
Networks
in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate)
作者:
Shadab Shahsavari
1
;
Leila Rezaie Shirmard
2
;
3
;
Mohsen Amini
4
;
Farid Abedin Dokoosh
2
;
5
;
dorkoosh@tums.ac.ir
关键词:
artificial
neural
network
;
drug delivery
;
Fingolimod
;
poly(3-hydroxybutyrate-co-3-hydroxyvalerate)
;
response surface methodology
;
training algorithms
刊名:Journal of Pharmaceutical Sciences
出版年:2017
6.
Prediction of limiting activity coefficients for binary vapor-liquid equilibrium using
neural
networks
作者:
Hesam Ahmadian Behrooz
a
;
ahmadian@sut.ac.ir
;
R. Bozorgmehry Boozarjomehry
b
;
brbozorg@sharif.edu
关键词:
Artificial
neural
network
;
Limiting activity coefficient
;
Binary systems
;
VLE
刊名:Fluid Phase Equilibria
出版年:2017
7.
Multilayer
ed Control of Alternative Splicing Regulatory
Networks
by Transcription Factors
作者:
Hong Han
1
;
2
;
6
;
Ulrich Braunschweig
1
;
6
;
Thomas Gonatopoulos-Pournatzis
1
;
Robert J. Weatheritt
1
;
3
;
Calley L. Hirsch
4
;
Kevin C.H. Ha
1
;
2
;
Ernest Radovani
1
;
2
;
Syed Nabeel-Shah
1
;
2
;
Tim Sterne-Weiler
1
;
Juli Wang
1
;
Dave O&rsquo
;
Hanlon
1
;
Qun Pan
1
;
Debashish Ray
1
;
Hong Zheng
1
;
Frederick Vizeacoumar
4
;
Alessandro Datti
4
;
Lilia Magomedova
5
;
Carolyn L. Cummins
5
;
Timothy R. Hughes
1
;
2
;
Jack F. Greenblatt
1
;
2
;
Jeffrey L. Wrana
2
;
4
;
Jason Moffat
1
;
2
;
Benjamin J. Blencowe
1
;
2
;
7
;
b.blencowe@utoronto.ca
关键词:
alternative splicing
;
splicing factors
;
transcription factors
;
spliceosome
;
chromatin
;
RNA interference
;
embryonic stem cells
;
neuroblastoma cells
;
SPAR-seq
;
high-throughput screening
刊名:Molecular Cell
出版年:2017
8.
Generalized extreme learning machine autoencoder and a new deep
neural
network
作者:
Kai Sun
kai.s@foxmail.com
Author Vitae
;
Jiangshe Zhang
;
jszhang@mail.xjtu.edu.cn
Author Vitae
;
Chunxia Zhang
cxzhang@mail.xjtu.edu.cn
Author Vitae
;
Junying Hu
hujunyingmm@163.com
Author Vitae
关键词:
Extreme learning machine
;
Generalized extreme learning machine autoencoder
;
Manifold regularization
;
Deep
neural
network
;
Multilayer
generalized extreme learning machine autoencoder
刊名:Neurocomputing
出版年:2017
9.
Prediction of heat capacities of ionic liquids using chemical structure based
networks
作者:
Ali Barati-Harooni
a
;
alibarati.2012@yahoo.com
;
Adel Najafi-Marghmaleki
a
;
Amir H Mohammadi
b
;
c
;
d
;
a.h.m@irgcp.fr
;
amir_h_mohammadi@yahoo.com
关键词:
Ionic liquid (IL)
;
Heat capacity
;
Chemical structure
;
Model
;
Radial basis function
neural
network (RBF-NN)
;
Multilayer
Perceptron
Neural
Network (MLP-NN)
刊名:Journal of Molecular Liquids
出版年:2017
10.
A novel data preprocessing method for boosting
neural
network performance: A case study in osteoporosis prediction
作者:
Theodoros Iliou
a
;
th.iliou@ct.aegean.gr
;
Christos-Nikolaos Anagnostopoulos
a
;
canag@ct.aegean.gr
;
Ioannis M. Stephanakis
b
;
stephan@ote.gr
;
George Anastassopoulos
c
;
anasta@med.duth.gr
关键词:
Data pre-processing
;
Neural
networks
;
Machine learning
;
Osteoporosis prediction
刊名:Information Sciences
出版年:2017
1
2
3
4
5
6
7
8
9
按检索点细分(1043)
题名(51)
关键词(124)
文摘(641)
按出版年细分(1043)
2027年及以后(17)
2017年(11)
2016年(37)
2015年(14)
2014年(6)
2013年(67)
2012年(78)
2011年(81)
2010年(56)
2009年(68)
2008年(63)
2007年(61)
2006年(47)
2005年(34)
2004年(44)
2003年(41)
2002年(31)
2001年(20)
2000年(35)
2000年及以前(232)
NGLC 2004-2010.National Geological Library of China All Rights Reserved.
Add:29 Xueyuan Rd,Haidian District,Beijing,PRC. Mail Add: 8324 mailbox 100083
For exchange or info please contact us via
email
.