Artificial Neural Network Approach for Main Engine Power Prediction of General Cargo Vessels

Before the physical constructing of a ship will start, it must first go through a multistage design process. Determining the ship's main engine power is a critical stage in the concept design process. This work established a model to predict for the main engine power of general cargo ships. The model input parameters included ship length overall, breadth, gross tonnage, DWT and ship service speed. In the training stage of the model, Levenberg-Marquardt optimization algorithm was used. After many training attempts with various numbers of hidden neurons, the structure with 22 hidden neurons showed the best performance. R values for the test set were 0.986, 0.988 for validation, and 0.992 for training, according to regression analysis. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values remained consistently low across all normalized datasets, ranging from 0.0128 to 0.0148 for MAE and 0.0178 to 0.0238 for RMSE. These results underscore the model's robust predictive capabilities.