Port Logistics Demand Forecast Based on Grey Neural Network with Improved Particle Swarm Optimization

Ruiping Yuan (), Hui Wei () and Juntao Li ()
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Ruiping Yuan: Beijing Wuzi University
Hui Wei: Beijing Wuzi University
Juntao Li: Beijing Wuzi University

A chapter in LISS 2020, 2021, pp 133-144 from Springer

Abstract: Abstract In order to improve the accuracy of port logistics demand prediction, the improved Particle Swarm Optimization algorithm, Grey Model and Neural Network are combined to construct an Improved Particle Swarm Optimization Grey Neural Network(IPSO-GNN) prediction model, in which the improved Particle Swarm Optimization algorithm is used to find the weight and threshold of the Grey Neural Network to improve the accuracy of the prediction. Using the logistics demand data of Dalian Port, the prediction effect of the proposed IPSO-GNN model is compared with that of the BP Neural Network model, the Grey model, the Grey Neural Network model and the standard Particle Swarm Optimization Grey Neural Network model. The empirical results show that the IPSO-GNN model has high precision and strong stability, which can predict port logistics demand effectively.

Keywords: Improved particle swarm optimization grey neural network; Grey relational analysis; Port logistics demand prediction (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-33-4359-7_10

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DOI: 10.1007/978-981-33-4359-7_10

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