scholarly journals Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

2014 ◽  
Vol 04 (02) ◽  
pp. 266-283 ◽  
Author(s):  
Lekan T. Popoola ◽  
Alfred A. Susu
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Expert System ◽  
Crude Oil ◽  
System Design ◽  
Distillation Column ◽  
Crude Oil Distillation ◽  
And Control

The Use of Quantitative Methods in Investment Decisions

2019 ◽  
pp. 256-275 ◽  
Author(s):  
Serkan Eti
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Quantitative Methods ◽  
Simulation Analysis ◽  
Investment Decision ◽  
Tree Algorithms ◽  
Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation

2013 ◽  
Vol 34 (4) ◽  
pp. 1169-1180 ◽  
Author(s):  
Majid Dehghani ◽  
Bahram Saghafian ◽  
Farzin Nasiri Saleh ◽  
Ashkan Farokhnia ◽  
Roohollah Noori
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Uncertainty Analysis ◽  
Artificial Neural

Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks

2007 ◽  
Vol 22 (3) ◽  
pp. 1202-1209 ◽  
Author(s):  
Armando M. Leite da Silva ◽  
Leonidas Chaves de Resende ◽  
Luiz AntÔnio da Fonseca Manso ◽  
Vladimiro Miranda
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Reliability Assessment ◽  
Artificial Neural ◽  
Composite Reliability

scholarly journals Neural Networks in Mechanical System Simulation, Identification, and Assessment

1993 ◽  
Vol 1 (2) ◽  
pp. 177-199 ◽  
Author(s):  
Thomas L. Paez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
System Design ◽  
Mechanical System ◽  
System Simulation ◽  
Design Analysis ◽  
Control Problems ◽  
Artificial Neural ◽  
And Control

Artificial neural networks (ANNs) have been used in the solution of a variety of mechanical system design, analysis, and control problems. This paper describes the ANNs that have been most frequently used in mechanical system applications. It also summarizes some of the applications that have been developed for ANNs, and briefly reviews the literature where descriptions of the developments and applications can be found. Some recommendations regarding ANN applications in mechanical system simulation, identification, and assessment are provided.

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