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

Abstract

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.