A comparison of artificial neural networks and multiple regression methods for the analysis of pilot-scale data

2004 ◽  
Vol 3 (Supplement 1) ◽  
pp. S45-S58 ◽  
Author(s):  
C W Baxter ◽  
Daniel W Smith ◽  
S J Stanley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Pilot Scale ◽  
Regression Methods ◽  
Artificial Neural ◽  
Scale Data

scholarly journals Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods

1970 ◽  
Vol 24 (1) ◽  
pp. 1-14
Author(s):  
Mustafa Özuysal ◽  
Gökmen Tayfur ◽  
Serhan Tanyel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Public Transportation ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Passenger Flow ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.   Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

scholarly journals Performance Prediction of a Pump as Turbine: Sensitivity Analysis Based on Artificial Neural Networks and Evolutionary Polynomial Regression

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3497 ◽  
Author(s):  
Gabriella Balacco
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Performance Prediction ◽  
Polynomial Regression ◽  
Input Parameter ◽  
Regression Methods ◽  
Reverse Mode ◽  
Evolutionary Polynomial Regression ◽  
Starting Point ◽  
Artificial Neural

The research of a general methodology to predict the pump performance in a reverse mode, knowing those of a pump in a direct mode, is a question that is still open. The scientific research is making many efforts toward answering this question, but at present, there is still not much clarity. This consideration has been the starting point of this research that thanks to artificial neural networks and evolutionary polynomial regression methods have tried to investigate and define the real weight of every input parameter, representing the efficiency of a pump in a direct way, on the output parameters, and representing efficiency of a pump used like a turbine.

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