Title :
Marine traffic accident prediction based on particle swarm optimization-based RBF neural network
Author_Institution :
Shandong Jiaotong Univ., Jinan, China
Abstract :
The future marine traffic accident situation is shown by using the marine traffic accident prediction method. Thus, marine traffic accident prediction method based on particle swarm optimization-based RBF neural network is presented in the paper. Particle swarm optimization algorithm, a kind of population-based optimization algorithm, is used to adjust the connection weights and the center and width of radial basis function. The marine traffic accidents of a certain terminal from 1996 to 2007 are applied to study the feasibility of the proposed PSO-RBF neural network. The comparison results between the proposed PSO-RBF neural network and normal RBF neural network can indicate that the prediction results of marine traffic accidents of the proposed PSO-RBF neural network are better than those of RBF neural network.
Keywords :
marine accidents; particle swarm optimisation; radial basis function networks; traffic engineering computing; PSO-RBF neural network; marine traffic accident prediction method; particle swarm optimization; population-based optimization algorithm; radial basis function neural network; Accidents; Approximation algorithms; Artificial neural networks; Optimization; Particle swarm optimization; Prediction algorithms; Prediction methods; marine traffic accident; neural network; particle swarm optimization; prediction model;
Conference_Titel :
Computer Research and Development (ICCRD), 2011 3rd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-839-6
DOI :
10.1109/ICCRD.2011.5764052