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

In: Liss 2020

Author

Listed:
  • Ruiping Yuan

    (Beijing Wuzi University)

  • Hui Wei

    (Beijing Wuzi University)

  • Juntao Li

    (Beijing Wuzi University)

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.

Suggested Citation

  • Ruiping Yuan & Hui Wei & Juntao Li, 2021. "Port Logistics Demand Forecast Based on Grey Neural Network with Improved Particle Swarm Optimization," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 133-144, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_10
    DOI: 10.1007/978-981-33-4359-7_10
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