DocumentCode
3096312
Title
Marine traffic accident prediction based on particle swarm optimization-based RBF neural network
Author
Yang Zhen-Qi
Author_Institution
Shandong Jiaotong Univ., Jinan, China
Volume
1
fYear
2011
fDate
11-13 March 2011
Firstpage
435
Lastpage
438
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Research and Development (ICCRD), 2011 3rd International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-61284-839-6
Type
conf
DOI
10.1109/ICCRD.2011.5764052
Filename
5764052
Link To Document