Data-Driven Neural Network Model for Robust Reconstruction of Automobile Casting

3D Research ◽  
2017 ◽  
Vol 8 (3) ◽  
Author(s):  
Jinhua Lin ◽  
Yanjie Wang ◽  
Xin Li ◽  
Lu Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Driven

scholarly journals Self-consistent electron–THF cross sections derived using data-driven swarm analysis with a neural network model

2020 ◽  
Vol 29 (10) ◽  
pp. 105008
Author(s):  
P W Stokes ◽  
M J E Casey ◽  
D G Cocks ◽  
J de Urquijo ◽  
G García ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Cross Sections ◽  
Neural Network Model ◽  
Data Driven ◽  
Self Consistent ◽  
Using Data

A novel construction method of convolutional neural network model based on data-driven

2018 ◽  
Vol 78 (6) ◽  
pp. 6969-6987 ◽  
Author(s):  
Guo-feng Zou ◽  
Gui-xia Fu ◽  
Ming-liang Gao ◽  
Jin Shen ◽  
Li-ju Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Construction Method ◽  
Data Driven ◽  
Model Based

scholarly journals A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

2008 ◽  
Vol 26 (12) ◽  
pp. 3945-3954 ◽  
Author(s):  
Y. Tulunay ◽  
E. T. Şenalp ◽  
Ş. Öz ◽  
L. I. Dorman ◽  
E. Tulunay ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Data Driven ◽  
Mapping Technique ◽  
Inference System ◽  
Cloud Coverage ◽  
Highly Nonlinear ◽  
Fuzzy Neural

Abstract. Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M). The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS), a data driven Neural Network module (METU-FNN) of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC) and cloud top temperatures (CTT) are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

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