Ship course prediction based on PSO combined with BP neural network

For a nonlinear process of ship course motion subjected to random disturbances, it is difficult to predict. A new approach based on particle swarm optimization and back-propagation neural network algorithm was proposed to predict ship course. Combined particle swarm optimization with back-propagation neural network, this method utilized easy to realize, fast convergence speed and high accuracy merit of particle swarm algorithm to optimize the structure of neural network, and solve the problem in BP neural network which is sensitive with the initial weights, easy to fall into the local least value. The PSO-BPNN course prediction model of ship motion was established, then the model of PSO-BP neural network and BP neural network to predict ship course separately. Simulation results demonstrated that the proposed algorithm had a faster convergence rate and higher accuracy than prediction method of BPNN.