engleski

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

Authors present a Multilayer Perceptron (MLP) artifi cial 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 conditionbased maintenance of naval propulsion plants dataset, out of which GT Compressor decay state coeffi cient and GT Turbine decay state coeffi cient 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 diff erent parameter combinations are trained using a grid search algorithm, quality of each solution being estimated with coeffi cient 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.

artificial intelligence ; artificial neural networks ; CODLAG propulsion system ; multilayer perceptron ; speed estimation

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