Vessel traffic flow prediction method based on ensemble empirical mode decomposition and back propagation neural network optimized with differential evolution algorithm

XIAO Jin-li; LI Xiao-lei

doi:10.16411/j.cnki.issn1006-7736.2018.02.002

2018 , Vol. 44 >Issue 2: 9 - 14

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2018.02.002

Vessel traffic flow prediction method based on ensemble empirical mode decomposition and back propagation neural network optimized with differential evolution algorithm

XIAO Jin-li ,
LI Xiao-lei

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(School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Hubei Key Laboratory of Inland Shipping Technology,Wuhan 430063,China)

Received date: 2017-09-14

Revised date: 2017-12-18

Online published: 2017-12-18

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Abstract

Aiming at the nonlinear and nonstationary characteristics of vessel traffic flow, a vessel traffic flow combination prediction (EEMD-DEBPNN) model was designed by assembling ensemble empirical mode decomposition (EEMD) algorithm and back propagation neural network optimized with differential evolution (DEBPNN) algorithm for predicting vessel traffic flow more accurately. Firstly, the EEMD method was used to reduce the nonstationary of vessel traffic flow time series, and then the DEBPNN model was used to predict the nonlinear components obtained after the decomposition of EEMD(Firstly, the DE algorithm was used to pre optimize the initial weights and thresholds of BPNN, and then the initial weights and thresholds obtained from pre optimization were used to train BP neural network to get the best weights and thresholds), and finally, the predictions for all components were added up and the accumulation result was namely the final prediction of the EEMD-DEBPNN model. Based on the statistical data of monthly vessel traffic flow from a certain port of Yangtze River, the EEMDDEBPNN prediction result was compared with those of BPNN and DEBPNN models, and the results show that the EEMD-DEBPNN model has higher prediction accuracy.

Key words： vessel traffic flow; ensemble empirical mode decomposition(EEMD); differential evolution algorithm(DE); back propagation neural network(BPNN); combination prediction

Cite this article

XIAO Jin-li , LI Xiao-lei . Vessel traffic flow prediction method based on ensemble empirical mode decomposition and back propagation neural network optimized with differential evolution algorithm[J]. Journal of Dalian Maritime University, 2018 , 44(2) : 9 -14 . DOI: 10.16411/j.cnki.issn1006-7736.2018.02.002

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