Genetic algorithm to improve Back Propagation Neural Network ship track prediction

Abstract

Abstract This paper studies a method that uses genetic algorithm to simultaneously optimize the number and weights of Back Propagation (BP) neural network neurons to predict the ship’s trajectory, so as to accurately predict the ship’s trajectory. The trajectory of the ship can be predicted by the neural network, but the selection of the setting parameters of the neural network requires rich experience and a large amount of attempts. In this study, the genetic algorithm (GA) is used to optimize the structure and weights of the neural network at the same time, avoiding the manual setting of parameters and improving the accuracy of the neural network prediction.