MATEC Web Conf.
Volume 314, 2020International Cross-Industry Safety Conference (ICSC) – International Symposium on Aircraft Technology, MRO and Operations (ISATECH) (ICSC-ISATECH 2019)
|Number of page(s)||11|
|Section||International Symposium on Aircraft Technology, MRO and Operations|
|Published online||29 May 2020|
Metaheuristics optimized machine learning modelling for estimation of exergetic emissions of a propulsion system
Eskisehir Technical University, Faculty of Aeronautics and Astronautics, TR-26470 Eskisehir, Turkey
2 Eskisehir Technical University, Faculty of Aeronautics and Astronautics, TR-26470 Eskisehir, Turkey
3 TUSAS Engine Industries, Eskisehir, Turkey
You This study offers a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a business jet aircraft’s exergo-emission parameters, such us exergy destruction ratio (rex,dest) and waste exergy ratio (rwex), at different flight stages. In consideration of this, the development of hybrid genetic algorithm (GA)-ANN models has been achieved by considering real databases of rex, dest and rwex at various power levels. Implementing a metaheuristics-based optimization on the generated multilayer perceptron (MLP) ANN models has produced the most favorable initial network weights, step-size, biases as well as training algorithm’s back-propagation (BP) momentum rate in addition to optimal quantity of neurons in the hidden layer(s) with regard to the topology design. In accordance with an error assessment approach, there exists a close fit linking the reference real data and rwex (linear correlation ratio, R, value of 0.999851) as well as rex,dest (R value of 0.999985) predicted values.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.