ANALYSIS OF POSSIBILITY OF USING NEURAL NETWORK TO FORECAST PASSENGER TRAFFIC FLOWS IN RUSSIA

At present the problem of forecasting passenger transport demand is of immense importance for air transport producers as well as for investors since investment efficiency is greatly affected by the accuracy and adequacy of the estimation performed. The aim of the present research is to analyze the possibility of using a neural network approach to forecast the expansion of the air‐transport network in Russia.

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The psychopharmacology of latent inhibition: A neural network approach

PsycEXTRA Dataset ◽

10.1037/e536982012-744 ◽

1997 ◽

Cited By ~ 1

Author(s):

Nestor A. Schmajuk ◽

Catalin V. Buhusi ◽

Jeffrey A. Gray

Keyword(s):

Neural Network ◽

Latent Inhibition ◽

Network Approach ◽

Neural Network Approach

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Escape and Avoidance Revisited: A Neural Network Approach

PsycEXTRA Dataset ◽

10.1037/e665412011-065 ◽

1992 ◽

Author(s):

Nestor A. Schmajuk ◽

David Urry

Keyword(s):

Neural Network ◽

Network Approach ◽

Neural Network Approach

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A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

Matériaux & Techniques ◽

10.1051/mattech/2019002 ◽

2018 ◽

Vol 106 (6) ◽

pp. 603 ◽

Cited By ~ 2

Author(s):

Bendaoud Mebarek ◽

Mourad Keddam

Keyword(s):

Neural Network ◽

Experimental Data ◽

Fuzzy Neural Network ◽

Treatment Time ◽

Calculation Model ◽

Average Error ◽

Network Approach ◽

Neural Network Approach ◽

Fuzzy Neural ◽

Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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