Metaheuristics optimized machine learning modelling for estimation of exergetic emissions of a propulsion system

• Published in: MATEC Web of Conferences 2020, Vol.314, p.2001
• Main Authors: Baklacioglu, Tolga, Turan, Onder, Aydin, Hakan
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Back propagation networks; Business aircraft; Design parameters; Exergy; General aviation aircraft; Genetic algorithms; Heuristic methods; Jet aircraft; Learning theory; Machine learning; Mathematical models; Multilayer perceptrons; Optimization; Propulsion systems; Topology
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/202031402001

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.