scholarly journals An approach for determining relative input parameter importance and significance in artificial neural networks

2007 ◽  
Vol 204 (3-4) ◽  
pp. 326-334 ◽  
Author(s):  
Stanley J. Kemp ◽  
Patricia Zaradic ◽  
Frank Hansen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Input Parameter ◽  
Artificial Neural

Comparing Conventional and Artificial Neural Network Models for the Pricing of Options

2002 ◽  
pp. 220-235 ◽  
Author(s):  
Paul Lajbcygier
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Parameter ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Conventional Models ◽  
Artificial Neural Network Models

The pricing of options on futures is compared using conventional models and artificial neural networks. This work demonstrates superior pricing accuracy using the artificial neural networks in an important subset of the input parameter set.

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.

scholarly journals Metamodeling of the Energy Consumption of Buildings with Daylight Harvesting – Application of Artificial Neural Networks Sensitive to Orientation

2021 ◽  
Vol 8 (2) ◽  
pp. 255-269
Author(s):  
Raphaela Walger da Fonseca ◽  
◽  
Fernando Oscar Ruttkay Pereira ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Input Parameter ◽  
Building Envelope ◽  
Building Energy Efficiency ◽  
Input Parameters ◽  
Artificial Neural ◽  
End Use ◽  
Daylight Harvesting

Daylight harvesting is a well-known strategy to address building energy efficiency. However, few simplified tools can evaluate its dual impact on lighting and air conditioning energy consumption. Artificial neural networks (ANNs) have been used as metamodels to predict energy consumption with high precision, few input parameters and instant response. However, this approach still lacks the potential to estimate consumption when there is daylight harvesting, at the ambient level, where the effect of orientation can be noted. This study investigates this potential, in order to evaluate the applicability of ANNs as a tool to aid the architectonic design. The ANNs were approached as metamodels trained based on EnergyPlus thermo-energetic simulations. The network configuration focused on determining its simplest feasible form. The input parameters adopted as the main variables of the building envelope were as follows: orientation, window-to-wall ratio and visible transmission. The effects of the encoding of orientation as a network input parameter, the number of examples of each variable for network training and changing the parameters used for the training were evaluated. The networks predicted the individualized consumption according to the end use with errors below 5%, indicating their potential to be applied as a simplified tool to support the design process, considering the elementary variables of the building envelope. The discussion of results focused on guidelines and challenges to achieve this purpose when contemplating the broadening of the metamodel scope.

scholarly journals Predicting fire resistance ratings of timber structures using artificial neural networks

2020 ◽  
Vol 14 (2) ◽  
pp. 28-39
Author(s):  
Pham Thanh Tung ◽  
Pham Thanh Hung
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Fire Resistance ◽  
Input Parameter ◽  
Training Algorithms ◽  
Ann Model ◽  
Wooden Floor ◽  
Artificial Neural ◽  
Input Variables

This paper describes a method to predict the fire resistance ratings of the wooden floor assemblies using Artificial Neural Networks. Experimental data collected from the previously published reports were used to train, validate, and test the proposed ANN model. A series of model configurations were examined using different popular training algorithms to obtain the optimal structure for the model. It is shown that the proposed ANN model can successfully predict the fire resistance ratings of the wooden floor assemblies from the input variables with an average absolute error of four percent. Besides, the sensitivity analysis was conducted to explore the effects of the separate input parameter on the output. Results from analysis revealed that the fire resistance ratings are sensitive to the change of Applied Load (ALD) and the number of the Ceiling Finish Layer (CFL) input variables. On the other hand, the outputs are less sensitive to a variation of the Joist Type (JTY) parameter. Keywords: artificial neural networks; fire resistance; wooden floor assembly; sensitivity analysis.

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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