Prethodno priopćenje
https://doi.org/10.17818/NM/2020/2.4
Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron
Sandi Baressi Šegota
orcid.org/0000-0002-3015-1024
; University of Rijeka, Faculty of Engineering
Ivan Lorencin
; University of Rijeka, Faculty of Engineering
Jelena Musulin
orcid.org/0000-0002-5213-1550
; University of Rijeka, Faculty of Engineering
Daniel Štifanić
orcid.org/0000-0001-9396-2441
; University of Rijeka, Faculty of Engineering
Zlatan Car
orcid.org/0000-0003-2817-9252
; University of Rijeka, Faculty of Engineering
Sažetak
Authors present a Multilayer Perceptron (MLP) artificial neural network (ANN) method for the purpose of estimating a speed of a frigate using a combined diesel-electric and gas (CODLAG) propulsion system. Dataset used is publicly available, as condition-based maintenance of naval propulsion plants dataset, out of which GT Compressor decay state coefficient and GT Turbine decay state coefficient are unused, while 15 features are used as input and ship speed is used as dataset output. Data set consists of 11934 data points out of which 8950 (75%) are used as a training set and 2984 (25%) are used as a testing set. 26880 MLPs, with 8960 different parameter combinations are trained using a grid search algorithm, quality of each solution being estimated with coefficient of determination (R2) and mean absolute error (MAE). Results show that a high-quality estimation can be made using an MLP, with best result having an error of just 3.4485x10-5 knots (absolute error of 0.00014% of the range). This result was achieved with a MLP with three hidden layers containing 100 neurons each, logistic activation function, LBFGS solver, constant learning rate of 0.1 and no L2 regularization.
Ključne riječi
artificial intelligence; artificial neural networks; CODLAG propulsion system; multilayer perceptron; speed estimation
Hrčak ID:
238052
URI
Datum izdavanja:
18.5.2020.
Posjeta: 1.200 *