摘要
为更精确地对港口或航道内船舶交通流量进行预测,分别建立BP神经网络预测模型和RBF神经网络预测模型进行仿真,并以宁波港船舶交通流量为例进行验证.结果表明,在宁波港现有发展基础和港口设施状况下,RBF神经网络用于宁波港船舶交通流量预测误差较小,预测值与实际值相近.
To forecast ship traffic volume in port or waterway precisely, back propagation （BP） and radial basis function （RBF） neural network forecasting models were established and simulated. Tests in Ningbo port show that RBF neural network has the smaller error in forecasting ship traffic volume, and the predictive value is close to actual one under the current basis and port facilities.
出处
《大连海事大学学报》
CAS
CSCD
北大核心
2009年第3期40-42,共3页
Journal of Dalian Maritime University
关键词
船舶交通流量
预测模型
神经网络
ship traffic volume
forecasting model
neural networks
