scholarly journals Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks

2021 ◽  
Vol 22 (11) ◽  
pp. 62-71
Author(s):  
Vasyl Lopushniak ◽  
Halyna Hrytsuliak ◽  
Anatoliy Bykin ◽  
Nadia Bordyuzha ◽  
Larysa Semenko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Panicum Virgatum ◽  
Prognostic Models ◽  
Panicum Virgatum L ◽  
Artificial Neural

Moisture Content Prediction in the Switchgrass (Panicum virgatum) Drying Process Using Artificial Neural Networks

2015 ◽  
Vol 33 (14) ◽  
pp. 1708-1719 ◽  
Author(s):  
Víctor Martínez-Martínez ◽  
Jaime Gomez-Gil ◽  
Timothy S. Stombaugh ◽  
Michael D. Montross ◽  
Javier M. Aguiar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Panicum Virgatum ◽  
Drying Process ◽  
Artificial Neural

Application of Artificial Neural Networks for Prognostic Modeling of Fire Resistance of Reinforced Concrete Pillars

2011 ◽  
Vol 148-149 ◽  
pp. 856-861 ◽  
Author(s):  
Marijana Lazarevska ◽  
Milos Knezevic ◽  
Meri Cvetkovska
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Fire Resistance ◽  
Prognostic Models ◽  
Standard Fire ◽  
The Neural Network ◽  
Problem Description ◽  
Artificial Neural ◽  
The Given

Artificial neural networks can be used for building prognostic models of various engineering problems. This paper presents an example of how we can predict the time of fire resistance based on the given experimental and numerical results. The analyses concerning the behavior of the reinforced-concrete construction elements during the standard fire, together with the basic theoretical information and detailed problem description, as well as the graphical curves for the fire resistance of the reinforced-concrete pillars, are given in the doctoral theses of Prof. Cvetkovska [3]. Using the concepts of artificial neural networks and the results of the performed numerical analyses as input parameters we made the prediction model for determination of the time of fire resistance of reinforced-concrete pillars. The neural network generated excellent results which will be presented further below in this paper.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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