Abstract
In order to improve the accuracy of ship course prediction. Using self-adapting particle swarm optimization algorithm (SAPSO) to optimize the network parameters of traditional BP neural network can overcome the shortcomings of BP neural network which are sensitive to initial weight threshold and easy to fall into local optimum. Furthermore, a neural network model of adaptive mutation particle swarm optimization algorithm is proposed for ship course prediction. Finally, the real-time forecasting simulation experiment of the ship was carried out by using the measured data of the MV YuPeng to verify the practicability and reliability of the SAPSO-BP prediction model.
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